Generation Sources Research Articles

An effective imaging based diagnosis of Solar Photovoltaic System and performance parameters for smart solar power grid

Another of the most attractive power generation sources for residential, corporate, and commercial operations is energy production. Due to this compelling qualities, photovoltaic systems (PV) electricity production have increasingly attracted the attention of researchers and professionals. Nonetheless, the greatest obstacle in generating energy from the sun is the predictable discontinuous output power from photovoltaic (PV) systems, which is largely caused by climate. The power differential of a photovoltaic panels may considerably reduce the financial profit of large scale solar fields. For everyday proper management of electricity supply production, distribution, and storing as well as for judgement on the electricity market, results were consistent prediction of the electricity output of solar PV systems is essential. The 5 subjects mentioned in this article are: The varied faults which might occur in PV panels; internet Photovoltaic panels monitoring; Use of machine learning approaches in Photovoltaic module damage detection; Advantages of fault diagnosis in PV panels and the various motion sensors used for this purpose are discussed. Recommendation for future possible research paths are given in view of the evaluated research.

Хроническая боль в спине: от патогенеза к адекватной фармакотерапии

Chronic back pain is an actual problem of clinical medicine, the pathogenesis of which includes peripheral nociceptive stimulation, peripheral and/or central inflammation, abnormality of the central pain modulation network and mixed onset of various pathological mechanisms. Sources of nociceptive signal generation include the intervertebral disc, muscles, fasciae, facet joints, sacroiliac joints, pubic symphysis, ligaments and joint capsule, and other structures that contain nociceptors. Any event that causes tissue degeneration activates a massive inflammatory response that infiltrates the MPD, joints, muscles, fascia and other tissues, stimulates nociceptive receptors to produce inflammatory substances, which directly damages the nerve root and causes pain. Simultaneously, the damaged MPD and endplate stimulate pathological and invasive growth and proliferation of vessels and nerves in the MPD. Increasing evidence also suggests that systemic inflammation plays a crucial role in the pathogenesis of chronic back pain. The central mechanism of back pain involves altered sensory processing in the brain. Dysfunction of the descending pain modulation system results in amplification of pain information in the brain. Since monotherapy for chronic back pain is not optimal, it is necessary to promote the comprehensive treatment recommended by current international clinical guidelines and it is reasonable to combine several therapies to improve the clinical effect of therapy. The combination of Tizanidine and Mirtazapine is an effective pharmacotherapy for chronic back pain. KEYWORDS: Chronic back pain, pathogenesis of dorsalgia, Tizanidine, Mirtazapine

Integrated Optimal Energy Management of Multi-Microgrid Network Considering Energy Performance Index: Global Chance-Constrained Programming Framework

Distributed generation (DG) sources play a special role in the operation of active energy networks. The microgrid (MG) is known as a suitable substrate for the development and installation of DGs. However, the future of energy distribution networks will consist of more interconnected and complex MGs, called multi-microgrid (MMG) networks. Therefore, energy management in such an energy system is a major challenge for distribution network operators. This paper presents a new energy management method for the MMG network in the presence of battery storage, renewable sources, and demand response (DR) programs. To show the performance of each connected MG’s inefficient utilization of its available generation capacity, an index called unused power capacity (UPC) is defined, which indicates the availability and individual performance of each MG. The uncertainties associated with load and the power output of wind and solar sources are handled by employing the chance-constrained programming (CCP) optimization framework in the MMG energy management model. The proposed CCP ensures the safe operation of the system at the desired confidence level by involving various uncertainties in the problem while optimizing operating costs under Mixed-Integer Linear Programming (MILP). The proposed energy management model is assessed on a sample network concerning DC power flow limitations. The procured power of each MG and power exchanges at the distribution network level are investigated and discussed.

Energy management in a grid-connected microgrid using hybrid Golden Jackal optimization and gradient descent optimization and the concept of loadability

Golden Jackal optimization (GJO) maintains diversity and can simultaneously explore multiple regions of the search space since it operates on a population of solutions. However, the GJO has the demerit of deprived exploitation capability and it struck in local optima easily. For enhanced optimization efficacy, the suggested approach integrates a hybrid optimization algorithm that combines gradient descent and the GJO algorithm. The goal of this hybrid strategy is to influence the complimentary advantages of both algorithms: the local exploitation potential of gradient descent and the global exploration capabilities of GJO. The suggested hybrid method uses GJO to initialize a population of possible results. Then, iteratively exploring the search space, it notifies solutions based on GJO's mechanisms for exploration and exploitation. Furthermore, a portion of the population undergoes refinement using gradient descent in order to effectively converge towards local minima by utilizing local knowledge. The hybrid algorithm demonstrates improved convergence speed, robustness to diverse search space characteristics, and effectiveness in handling large-scale optimization problems compared to various optimization techniques including particle swarm optimization, pelican optimization, artificial bee colony, whale optimization algorithm and grey wolf optimization, standalone GJO and gradient descent algorithms. The total cost of the MG with the proposed methodology has reduced to 12017 rupees from 12350 by gradient descent and 12332 using GJO. Detailed comparison of various optimization techniques has been presented in the results section. Further, this paper also addressed the concept of loadability, which refers to the MG's ability to meet electricity demand under varying conditions, relying solely on its internal resources. Achieving optimal loadability in isolated MGs involves diversifying generation sources, leveraging energy storage systems, implementing demand-side management measures, utilizing predictive analytics, and designing resilient grid infrastructure. By implementing Bayesian sparse polynomial chaos expansion, the concept of loadability has been assessed. Due to which the MG operators can enhance energy management capabilities and maximize loadability, ensuring reliable and sustainable electricity supply to connected loads.

Real-time techno-economical operation of preserving microgrids via optimal NLMPC considering uncertainties

In the modern era, managing optimal real-time control of microgrids during the operation phase has been a significant challenge, requiring careful consideration of both technical and economic factors. This paper introduces a framework for the real-time control of islanded microgrids using a preserving network. This structure incorporates various distributed generation sources, including rotating and non-rotating resources, along with energy storage systems. The optimization function within model predictive control (MPC) manages essential network parameters, such as frequency and voltage, while addressing real-time economic and technical objectives. To enhance precision and account for uncertainties in generation and consumption parameters, the integration of continuous power flow and the preserving network model is employed. This approach aims to create a model that closely mirrors real-world conditions, ensuring a more accurate representation of microgrid dynamics. The proposed structure demonstrates significant improvements in both technical and economic performance compared to Standard MPC and Adaptive MPC, highlighting its potential for more efficient islanded microgrid management. The proposed framework achieves notable reductions in total voltage deviation of 85.87% and 87.62% compared to Standard MPC and Adaptive MPC, respectively. Additionally, it delivers impressive enhancements in frequency deviation of 99.46% and 96.62% compared to Standard MPC and Adaptive MPC, respectively. Economically, the proposed framework significantly outperforms both, reducing costs by 39.29% compared to Standard MPC and by 28.12% compared to Adaptive MPC.

Research on monitoring and energy management systems for energy storage stations on the power generation side

Abstract Addressing the challenges of limited operational efficiency, inadequate battery safety, inflexible system scalability, inadequate AGC/AVC functionality, and low levels of intelligent operation and maintenance encountered by energy storage power stations integrated with new energy generation sources, this article delves into the pivotal role of these power stations on the generation side. It meticulously examines the network architecture, functional framework, and pivotal technologies underpinning the system. Furthermore, the article proposes a comprehensive monitoring and energy management system tailored specifically for energy storage on the generation side. The system uses micro-service architecture and container technology to realize the processing and storage of massive data. Through complete isolation of the information network and control network and millisecond communication with GOOSE, the charging and discharging response speed of the energy storage station is greatly improved. The power allocation algorithm based on device status recognition completes the system’s high-precision power control of PCS, and this system uses big data analysis and data mining technology to conduct a comprehensive statistical analysis of battery health status, power station operation efficiency, and equipment operation status and energy consumption. The design of the system has favorable technical and application guidance for the subsequent energy management design and engineering application of the energy storage power station.

Нейровизуализационный подход для выявления биомаркеров рабочей памяти у больных с хронической ишемией мозга

Verbal working memory (VWM) is a fundamental function responsible for temporary storage and short-term handling of verbal information. The study was aimed to determine the working memory biomarker associated with imaging of the source of infra-slow electrical activity in patients with chronic cerebral ischemia (CCI). A total of 50 patients with CCI took part in the study: 16 males and 34 females aged 50–85 years. VWM was evaluated by the Luria test. The subjects were divided into two groups matched by age with the VWM below and above the average level for the studied sample. The infra-slow, below 0.1 Hz, electrical activity, otherwise known as the DC potentials (DCPs) of the brain, was recorded with five monopolar leads: frontal, central, occipital, right and left temporal. The resting state fMRI was used to analyze brain regions with the activated BOLD (blood-oxygen-level-dependent) signal that were associated with the brain regions responsible for VWM and the DCP generation sources recorded with the non-polarizable electrodes. The differences in BOLD signal activation and infra-slow activity amplitude were found in two VWM groups. These resting-state neural networks, VWM and the neural network responsible for DCP generation, overlapped in frontal regions. There were significant differences in DCP recorded with the frontal lead in two VWM groups (р = 0.00004). In patients with CCI, infra-slow activity, recorded with the frontal lead that is generated by the neural network fragment representing an intersection of the VWM network and the part of the brain responsible for DCP generation in the frontal region, is a VWM biomarker.

Determining biomethane potential from animal-source industry wastes by anaerobic digestion: A case study from La rioja, Spain

The animal-source food industry (ASFI) is currently facing important challenges derived from sustainability policies and market requirements. In this respect, the ecological footprint resulting from emissions, management of residues, effluents, and by-products has become a key factor not only due to the laws in force, but also from the point of view of operation efficiency and market requirements. This paper assesses the potential use of the organic wastes generated by the ASFI as a source of biofuel generation (i.e., biomethane) in the region of La Rioja (Spain). To this end, primary data were directly collected from factories and the so-produced wastes were sampled and characterized. Statistical inference from collected data leads to theoretically estimate that the region might produce up to 118.46 GWh per year, mainly provided from piggeries, which would replace 12.4 % of the natural gas used by the regional industry. Besides, this strategy also reduces the environmental footprint and the generation of added value for companies and rural localities. However, the analytics show that an excess of nitrogen content may hinder the calculated theoretical methane yields and lead to operational issues. Looking for biowaste from vegetable sources more optimal admixtures may be designed to address these concerns.

Determining Fault Location in Transmission Lines Using Differential Equation Algorithms

A transmission line, in the context of electrical engineering and power systems, is a high-voltage or high-tension line that is used to transmit electrical power over long distances from power generation sources (such as power plants) to substations, where the electricity is then distributed to homes, businesses, and industrial facilities. Transmission lines are the backbone of the electrical grid, serving as the highways for electricity delivery. Their efficient and reliable operation is essential for ensuring that electricity is generated, transmitted, and distributed effectively, meeting the needs of consumers while maintaining grid stability and resilience. Transmission line protection refers to a set of techniques and devices used in electrical power systems to detect and respond to faults or abnormal conditions that can occur in high-voltage transmission lines. These lines are a critical part of the electrical grid and are responsible for transporting electricity over long distances from power generation plants to distribution substations. This paper proposes a different differential equation algorithm to locate faulty section on transmission lines. As seen from performance curves, the proposed protection algorithm is able to distinguish normal and faulty conditions.

Effects of strain and pressure on entropy generation in laminar flames

The mitigation of combustion noise is crucial for preventing thermoacoustic instabilities in rocket and gas turbine engines. Entropy noise, arising from entropy fluctuations within the engine, serves as a prominent noise source. Therefore, understanding the mechanisms governing entropy generation in flames becomes imperative for identifying the roots of indirect combustion noise. While previous studies have broadly examined various pathways of entropy generation under different canonical configurations of laminar flames, surprisingly, the effect of strain and pressure has been overlooked. This aspect is analyzed in the present work. Building upon previous analysis, the entropy source term of the reacting flow is decomposed into three sub-terms: the unsteady heat release term (Se,1), the sensible enthalpy term (Se,2), and the partial entropy term (Se,3). The extent to which these three sub-terms contribute to entropy generation is thoroughly analyzed. Simultaneously, the scaling laws of the three sub-terms with respect to strain rate and pressure are investigated. The inclusion of detailed chemical kinetics as well as flame structure in the simulation dataset allows for in-depth investigation into the mechanism of the strain and pressure effects. Three canonical laminar flame configurations – the freely propagating premixed flame, the premixed counterflow flame, and the counterflow diffusion flame – are investigated. The following main conclusions are drawn: the heat release term is the leading constituent of the entropy source; the sensible enthalpy term is negligible; and the partial entropy term is secondary while non-negligible. Under strain and pressure variations, the contribution of the partial entropy term to total entropy production can greatly off-set the total entropy source or even qualitatively affect the strain/pressure response of the entropy generation, i.e., the strain response of the premixed flame and pressure response of the hydrogen non-premixed flame.Novelty and significance statement1. Conducted a systematic investigation of entropy production due to chemical reactions under the influence of strain and pressure variations.2. Revealed that the partial entropy term, often overlooked in previous studies, can surpass the heat release term in certain configurations, emerging as the leading constituent of the total entropy generation source.3. Identified dominating reactions contributing to entropy production for two different fuels of hydrogen and methane.

Prioritization of renewable energy for offshore ship charging stations based on intuitionistic fuzzy GLDS method: A case of China

Offshore ship charging station (OSCS) projects can help to address the current demand for electric ships for ocean voyages to a large extent. The proper selection of the energy source for power generation is a key part of the OSCS project. To select the optimal renewable energy for OSCS with many difficulties such as the ambiguity of the decision-making environment, the differences in group assessment information, and the conflict and compensation between criteria, this paper proposed a fuzzy multi-criteria decision-making (MCDM) framework. First, a comprehensive criteria system was constructed. Second, the intuitionistic fuzzy set (IFS) was introduced to express experts’ fuzzy cognition. Third, based on the quality of evaluation, a novel expert weighting method was proposed, and the generalized intuitionistic fuzzy weighted geometric interaction averaging (GIFWGIA) operator was used to aggregate the individual evaluations. Fourth, the criteria importance through intercriteria correlation (CRITIC) method, and the stepwise weight assessment ratio analysis II (SWARA II) method were introduced to determine the criteria weights. Fifth, considering criteria compensation, and individual and group ranks, the gained and lost dominance score (GLDS) method were used for ranking. Finally, to verify the applicability and reliability of the framework, a case study was conducted in Pingtan Island, Fujian Province. The results show that wind energy was the best alternative, followed by solar, wave and nuclear energy.

A new hybrid fixed and adaptive gains control algorithm to reduce power losses on LLCL filter-based renewable energy conversion systems with systematic parametrization using Grey Wolf optimizer

Currently, there is a transition in the energy matrix around the world, where traditional sources of energy generation are continually being replaced by energy generation systems based on renewable sources to mitigate the climate crisis. In this bias, this work presents the mathematical modeling of an LLCL filter, used to connect power generation systems based on renewable energy sources to the electrical grid, and presents a novel hybrid fixed-and adaptive gains control strategy for current injection into the grid using this system. The developed hybrid controller is composed of a proportional–integral controller and a direct robust adaptive controller. The first term of the controller guarantees the reference current, while the second term of the controller is used for disturbance rejection. Furthermore, a systematic procedure for the controller’s parametrization based on Grey Wolf Optimizer is also provided. The control of the current injected into the grid is carried out considering the LLCL filter without passive damping resistors in the filter structure to avoid power losses due to the passive filter elements. Additionally, the LLCL filter model considers minimal parasitic resistances to evaluate the controller’s performance and optimize it for the application of interest, aiming to maximize the system performance by ensuring a short transient regime due to the fast closed-loop system response. Simulation results indicate high performance of this optimized control strategy with small tracking error even considering grid impedance variations.

Diffusion behavior and transport risk of bioaerosol particles in a domestic waste landfill site in an arid and cold region of northwestern China

Bioaerosols have attracted increasing attention as novel contaminants because of their potential role in the spread of disease. In this study, sampling sites were established in a landfill in northwestern China with the aim of investigating the emission and diffusion characteristics of bioaerosols. The results revealed that the counts of airborne bacteria released by landfill cover area (LCA) and waste dumping area (WDA) located in the landfill area reached 18 193 ± 30 CFU/m3 and 10 948 ± 105 CFU/m3, respectively. These two aeras were the main sources of bioaerosol generation. Meanwhile, Corynebacterium spp., Bacteroidetes spp., and Pseudomonas spp. were identified as potential pathogens. A Gaussian model was applied to simulate the diffusion of the bioaerosols; the influence distance was calculated as 12 km from the boundary of the landfill site. The potential health risks of bioaerosol exposure to on-site workers and nearby residents were calculated and evaluated in terms of aerosol concentration, particle size, and pathogenic bacteria. The present study promotes the recognition of the emission behavior of microorganisms in aerosol particles and provides a basis for controlling bioaerosol contamination from landfill sites, particularly those located in cold and arid northwestern regions of China.

Transition to a 100 % renewable power supply in galapagos islands: Long-term and short-term analysis for optimal operation and sizing of grid upgrades

Owing to their remarkable biodiversity and endemic ecosystems, the Galapagos Islands are one of the most important World Heritage sites. However, due to their isolation and remote geographical location, around 83.6 % of the energy supply in Galapagos relies on the use of fossil fuels, which are polluting the air, the land and the sea, therefore requiring an adequate energy planning for the efficient transition towards a more sustainable and environmentally friendly energy sources. Although there were several previous studies of the decarbonization of Galapagos’ energy supply, there is a need for a more comprehensive approach that will combine planning (long-term) and operational (short-term) analysis to evaluate both, the future investment plans and performance of evolving energy supply system, as well as to provide an in-depth evaluation of required system upgrades in terms of locally available renewable energy resources. In order to fill this gap, this paper presents results of two different studies for the optimal decarbonization and transition to a 100 % renewable energy supply of the Galapagos Islands, giving details on the development of modelling tools required for the analysis of different scenarios and various techno-economic constraints. The first study is used for a long-term for analysing and strategic planning of the energy matrix until 2050, starting from existing power plants and following projections for the deployment of new renewable-based energy generation and dedicated energy storage sources to determine the optimal sizing and timing of required grid upgrades. The second study is a short-term annual performance analysis, which considers days of minimum, average and maximum available renewable energy resources to optimize system operation with existing and additional renewable energy generation and storage sources that will minimize, or keep at a certain target level, contributions from diesel generation. The two presented studies complement each other, allowing to evaluate transition to a 100 % renewable energy supply in Galapagos from both the investment/planning perspective and from the operational point of view. The results of two studies are very close in terms of the sizes of required new PV generation and associated energy storage in Baltra-Santa Cruz islands by 2050 (around 50 MW of PV and 145 MW of storage), with the short-term study allowing to identify curtailed PV generation (on average around 140 MWh per day) as an opportunity to connect new controllable loads, and with the long-term study allowing to evaluate interim targets for decarbonization pathways to Net Zero by 2050 through the related techno-economic optimization.

Design of Manganese‐Doped Zinc Oxide Nanoparticles as Effective Electrocatalytic System in Oxygen Reduction Reactions

ABSTRACTFuel cell technologies constitute a clean, reliable, highly efficient, and eco‐friendly source of alternative energy generation. However, they still require a reliable and robust catalytic system showing comparative electrochemical activity to precious metal Pt with less cost. In this work, Mn@ZnO NPs were synthesized using a hydrothermal‐assisted simple method. Several techniques including SEM, TGA, and XRD were used to confirm the material synthesis. Electrochemical properties were analyzed by using linear sweep voltammetry, chronoamperometry, and cyclic voltammetry. A higher ORR activity in terms of mass activity and current density was observed 133.9 mA/mg as compared to Pt/C (96 mA/mg) and Pd/C (67 mA/mg) under otherwise identical conditions. Mn@ZnO also exhibited excellent current density (1.913 mA cm−2), comparable to Pt/C (1.55 mA cm−2). Chronoamperometry shows stability for up to 800 s. Comparative studies were conducted in both acidic and basic mediums, with observed higher ORR activity in acidic media.

Distributed multienergy and low-carbon heating technology for rural areas in northern China

In the context of China's “double carbon” policy and “rural revitalization” strategy, clean and low-carbon heating in rural areas of the northern China has become a key problem that needs to be urgently addressed. Practice has proved that “coal-to-gas” and “coal-to-electricity” projects have various limitations, such as high operating cost, high carbon emission, and high cost of distribution network upgrade, making them unsuitable for nationwide promotion. To address this issue, this research proposes a distributed multienergy and low-carbon heating (DMLH) system, which integrates the photovoltaic power generation, combined heat and power (CHP) system, heat storage, and newly developed multisource efficient heat pump (MEHP). The MEHP operates by simultaneously absorbing heat from air and waste heat sources of the CHP generation, providing a stable and efficient heat output. The multienergy complementary operating strategy of the DMLH system has been presented, considering variations of heating demand and environmental temperature. Comprehensive case studies were performed under typical and extremely cold winter scenarios to examine the effectiveness of the DMLH system. Simulation results revealed that 66.7 % of the operating cost can be reduced by the proposed system in typical scenario and the heat supply stability of MEHP has been considerably enhanced compared to a single-stage heat pump system. Furthermore, the DMLH system has low-level requirement of distribution network capability, which is conducive to promotion in the rural areas of northern China.

Support for Advanced Nuclear Power Growing in the United States

Global clean energy and decarbonization goals are forcing governments around the world to take a hard look at their energy resource portfolios and future plans for meeting increasing electricity demand. While the last several years have seen a significant uptick in the deployment of solar, both terrestrial and offshore wind, and battery storage here in the United States, nuclear energy is once again being considered a critical energy generation source. Nuclear power, and in particular small modular nuclear reactors, are being considered as carbon‐free power generation sources to strategically meet new loads.

Deciphering the Nature and Dynamics of Gig-Platform Jobs: Workers’ Hidden Precarity

The technology-driven gig-platform sector has emerged as a new source of employment generation both globally as well domestically. This recent transformation in the labour market is reshaping the nature of labour practices, labour relations, workers’ rights, and contracts. The sector has huge potential to generate millions of job opportunities by leveraging the use of digital technology. As this sector continues to generate more jobs, such jobs are portrayed as fostering economic growth, while creating ‘meaningful jobs,’ which are mutually beneficial to workers and employers in terms of providing ‘flexibility and freedom,’ ‘better earning opportunity,’ and ‘promoting social inclusion,’ by which it implies that women are increasingly equipped to find better jobs. This article critically examines the developmental roles of platform jobs which are being particularly highlighted within the policy circle, in academic literature, and tech companies through workers’ lens. It delves deeper into the discussion on those very aspects of platform jobs just listed, including the flexibility and freedom debate, workers’ income, and the gender aspect of jobs. In doing so, it carefully examines these aspects with respect to their implications on workers in terms of working conditions and regulatory aspects. The article brings out the workers’ precarity hidden within those developmental aspects of gig-platform jobs.

A Real-Time Power Sharing Strategy for a Multi-Energy System While Considering Individual System Dynamics

For a sustainable and resilient energy supply, multi-energy systems (MES) are becoming more prominent. Real-time efficient power-sharing in an MES consisting of different generation sources, uncontrollable loads, multiple storage options, and Power-to-X technologies is a challenging task. The challenge arises due to constantly fluctuating generation and load, as well as the different system dynamics of each element in the setup. Optimizing the power-sharing among the various controllable elements in an MES could be broken down into a two-level process. The top-level process, commonly known as super-ordinate control, defines the power-sharing over a longer timeframe based on load and generation forecasts and multiple other factors. The lower-level process, commonly known as sub-ordinate control, based on the inputs from the super-ordinate control and live sensor data refines the control signals of the individual elements. This work focuses on developing a control framework for the sub-ordinate control process while considering the individual element dynamics such as dead time and rise time to a control signal. This is important as not considering differences in the system dynamics results in sub-optimal control, causing dynamic mismatches. This work proposes a simple rule-based power-splitting method backed up with individual PID regulators and Smith Predictor for each element in the MES, which are further coupled to each other for more precise and efficient control. The proposed method induces a cooperative behavior among the MES elements and improves the reaction time to a control signal while also improving the target tracking by 3-8%.

High-energy noise-like pulsing in a thulium/holmium co-doped fiber laser with watt-level average output power

A Thulium/Holmium co-doped fiber laser with high-energy noise-like rectangular pulses at the central wavelength of 1985 nm is experimentally demonstrated. The experimental setup is based on a 298-m long nonlinear optical loop mirror. By varying the pump power from 3 to 10 W, the noise-like rectangular pulse width can be tuned from 2.8 to 15.2 ns, respectively, and under a maximum pump power of 10 W, 1.45 W of average output power is obtained. The pulse repetition rate is 671 kHz. Consequently, highly energetic optical pulses with 2.16 µJ pulse energy and an estimated peak power of 142 W are achieved. To the best of our knowledge, these pulses, which are generated directly from the laser cavity, possess the highest average output power and pulse energy for noise-like pulse emission in the near 2 µm wavelength region. The proposed laser source has the most straightforward all-fiber cavity design that has been proposed for generating high-energy noise-like rectangular pulses, and as such has potential applications in scientific research and as a pump source for mid-infrared supercontinuum generation.