Micro-energy Systems Research Articles

The State-of-the-Art Fundamentals and Applications of Micro-Energy Systems on Chip (MESOC)

The State-of-the-Art Fundamentals and Applications of Micro-Energy Systems on Chip (MESOC)

Numerical study on combustion and energy efficiency characteristics of thermophotovoltaic-thermoelectric two-stage utilization system filled with porous media

Addressing the issues of low energy conversion efficiency and unstable combustion at the micro-scale resulting from significant energy loss in micro-energy systems, this paper proposes a two-stage utilization system integrating thermo-photovoltaic-thermoelectric technology filled with porous media. The research explored the impact of the placement, thickness, and porosity of porous media on both combustion and energy efficiency within a two-stage utilization system. Performance evaluation indexes such as temperature distribution, pressure drop loss, output power, and efficiency revealed the combustion characteristics of porous media at the micro-scale. The results indicate that the mean outer wall temperature of MTPV increases by 174.3 K as the porous media transitioned from a partially filled to a fully filled state. After comprehensive consideration of various factors, the optimal layout parameter for porous media is determined to be a two-stage uniform filling of porous media (SS304 with a porosity of 0.86 and a thickness of 14 mm). Consequently, the two-stage combustion system achieves an output power of 14.88 W and an energy conversion efficiency of 7.33 %.

Capacity optimization allocation method of photovoltaic-temperature difference-hydrogen-storage hybrid micro-energy system based on SAO algorithm

The capacity optimization allocation of hybrid micro-energy systems is an important link in micro-energy systems, which can effectively improve the reliability and economy of the power grid. In this paper, the capacity optimization allocation method of a photovoltaic-temperature difference-hydrogen-storage hybrid micro-energy system is studied. First, a capacity optimization configuration model based on photovoltaic, temperature difference, and hydrogen storage hybrid micro-energy system was established to maximize revenue. Then, the SAO algorithm is used to optimize its strategy. When the strategy does not change, it indicates that the income has been maximized. Finally, concerning the snow ablation mechanism, it can balance development and exploration well and can optimize the global optimal solution even in a more complex environment. The proposed method can ensure the reliability under premise of relatively low cost, and effectively improve the rationality of the power grid capacity allocation method in the photovoltaic thermal power generation scenario.

Assessing environmental impact: Micro-energy network optimization in a Chinese industrial park

Micro-energy systems contribute significantly to environmental improvement by reducing dependence on power grids through the utilization of multiple renewable energy sources. This study quantified the environmental impact of a micro-energy network system in an industrial park through a life cycle assessment using the operation of the micro-energy network over a year as the functional unit and “cradle-to-gate” as the system boundary. Based on the baseline scenario, a natural gas generator set was added to replace central heating, and the light pipes were expanded to constitute the optimized scenario. The results showed that the key impact categories for both scenarios were global warming, fine particulate matter formation, human carcinogenic toxicity, and human non-carcinogenic toxicity. The overall environmental impact of the optimized scenario was reduced by 68% compared to the baseline scenario. A sensitivity analysis of the key factors showed that electricity from the power grid was the key impact factor in both scenarios, followed by central heating and natural gas. Therefore, to reduce the environmental impact of network systems, it is necessary to further optimize the grid power structure. The research approach can be used to optimize micro-energy networks and evaluate the environmental impact of different energy systems.

Enhancing Sustainability and Efficiency in Offshore Oil and Gas Engineering through the Integration of Chaotic Local Search and Particle Swarm Optimization for Microenergy Systems Optimization

The offshore oil and gas industry is under increasing pressure to reduce carbon emissions while maintaining energy reliability. Offshore oil and gas platforms (OOGPs) face significant challenges in integrating low‐carbon operations with their energy systems. This study introduces an optimized scheduling approach for offshore microintegrated energy system (OMIES) that incorporates a hybrid energy storage system, including a floating power‐to‐gas associated gas storage (FP2G‐AGS) module, to address the intermittency of renewable energy sources. An economic optimization model is formulated, accounting for carbon emissions, operational costs, and the status of gas turbine generator sets. To solve the complex optimization problem, this study develops a hybrid chaotic local search and particle swarm optimization (CLPSO) algorithm. The CLPSO algorithm synergizes the global search ability of PSO with the local refinement of chaotic local search, enhancing the convergence to optimal solutions. Experimental results demonstrate that the proposed CLPSO algorithm effectively achieves optimal solutions within a range of 48.2–51.7. Case studies validate the model’s capability to promote new energy integration, reduce operational costs, and decrease CO2 emissions across various scenarios. This research significantly contributes to achieving low‐carbon operations on OOGPs and promotes the sustainable development of marine resources.

Climate change mitigation in energy-dependent regions—A carbon tax-based cross-system bi-layer model with equilibrium-optimization superposition effects

Solving the dilemma caused by the competing goals of low-carbon development and mitigating climate change has become a topic of great concern. However, previous studies have not fully characterized the mutual feedback mechanisms between macro-socioeconomic systems and micro-energy systems in response to carbon taxation. Based on a top-down computable general equilibrium model and a bottom-up optimization model, we constructed a carbon tax-based cross-system bi-layer model (CG-EM) that includes equilibrium-optimization superposition effects. The feasibility and effectiveness of carbon taxes are considered for Shanxi Province, China, a typical energy-dependent region. (1) The findings of this study were in contrast to the general assumption that carbon taxes negatively impact socioeconomic structures. (2) A reasonable carbon tax can promote the stability of an energy network, synergistically mitigate carbon and air pollutants, and optimize production efficiency and the structure of the energy system. (3) Based on an energy–water–carbon nexus mechanism, water scarcity and uneven spatio-temporal energy allocation can be alleviated. This study contributes to the theory of climate change mitigation by analyzing the effects and specific implementation pathways of carbon taxes.

Principles of resource-process modeling of territorial communities combined energy supply in the climate change prevention context

The research is devoted to the theoretical and practical substantiation of the principles of formation of systems of combined energy supply with heterogeneous sources to increase the energy efficiency of territorial communities in the conditions of climate change resistance. New approaches and structural models for the formation of local microenergy systems are proposed, which will encourage territorial communities to a new type of activity related to their own energy generation based on the use of local types of fuels and renewable energy sources. The principles of building such local energy supply systems, in general, consist in the rational distribution of resources (renewable sources, sources of combined energy generation using biofuels, energy from external networks) and coordination of the processes of generation, transmission, accumulation, distribution and consumption of energy within the boundaries of a separate territorial community by creating its own high-tech infrastructure assets. The formation of the energy balance in the medium and long term is proposed to be carried out on the basis of a resource-process analysis of the operation of local networks by finding the optimal use of local types of biofuels and renewable sources in the combined generation of electricity and heat, taking into account their current value. The cost of energy for the end consumer is determined by differentiated tariffs of energy supply companies and the use of several heterogeneous sources that participate in the process of decentralized energy production. Mathematical relations are obtained for calculating the values of the time intervals of the execution of a set of parallel processes that arise during the interaction of competing sources of distributed generation with end consumers and establishing the regularities of the formation of the energy balance a micro energy system. The proposed method can be a tool for finding optimal energy efficiency management scenarios of facilities with local energy supply, using operational redistribution of energy flows in the system of combined energy generation and forecasting current costs for energy supply in calculation periods. Such a concept can be a theoretical basis for the formation of principles of energy independence of territorial communities. Keywords: territorial community, combined-heat-and-power-system, renewable energy sources, biofuel, energy balances.

A Robust High-Quality Current Control With Fast Convergence for Three-Level NPC Converters in Microenergy Systems

Three-level neutral-point-clamped (3L-NPC) power converters are necessary interfaces to form micro-energy systems. Naturally, designing a suitable control scheme, featuring superior dynamics, strong robustness, and simple structure, is a promising solution to guarantee more efficient operation of converter. This paper proposes a robust high-quality current control strategy for the 3L-NPC power converter in the stationary <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\alpha \beta$</tex-math></inline-formula> frame. A super-twisting algorithm coupled with a Luenberger observer current controller is proposed to deal with the poor sinusoidal current tracking issue due to the existing inductance/grid frequency deviations and the disturbance of the sinusoidal dynamic nature. Additionally, an extended sliding mode disturbance observer based proportional control is built to dramatically enhance voltage regulation performance, in the case of capacitance deviations and unknown dc-loads. Experimental data confirm the effectiveness of the proposed solution outperforms the conventional proportional-resonant/-integral control in terms of accurate tracking current/voltage, anti-disturbance, and grid current total harmonic distortion.

Preface

The 2023 2nd International Conference on Smart Energy and Energy Internet of Things (SEEIoT 2023), held from June 16th to 18th, 2023 in Guilin, China (virtual event), was a premier academic conference on smart energy and energy Internet of Things for research groups working on related fields. It allowed researchers to share their experiences with other groups from all over the world by presenting scientific work and potential innovations in this research area and in the social events promoting informal get togethers.The works presented in this paper volume cover a broad range of topics including Actuators and Sensors for Smart Grids, Power Grid Dispatching and Automation Control, Ubiquitous Electric Internet of Things, Intelligent Energy Management, etc.The Conference was intended to give students and research groups an opportunity to learn more about smart energy and energy Internet of Things as an important tool in their applications and development. It represented a unique opportunity to meet colleagues and friends, exchanging ideas, and learning about new developments and best practice, while working to advance the understanding of the knowledge base that we will collectively draw upon in the years ahead to meet future challenges. Participants attended the academic presentations by scholars representing both institutes and academia.The Proceedings of SEEIoT 2023 included some excellent papers selected via a peer review process. The Conference Program featured keynote speeches by three world-leading scholars and many sessions for regular paper oral and poster presentations, attracting over 60 participants. Professor Zhenbin Zhang from Shandong University, China gave a speech on Bias-Free Fast Start-Up Strategy and High-Performance Predictive Control Method for DC Energy Routers. Energy routers based on Dual Active Bridge (DAB) converters are key components of hybrid micro-energy systems. They facilitate efficient bidirectional energy flow, and flexible integration of renewable energy sources. Three technical shortcomings are associated with conventional control methods for the DAB converter, viz., poor startup performance, slow dynamic response, and poor parameter robustness. His research in deed proposed effective solutions to these challenges.In making this Conference possible, we want to acknowledge our deep appreciation to all our conference authors for their excellent contributions and reviewers for their hard work on reviewing all the submissions; to all the Committee members for their effort and enthusiasm. Particular thanks go to the staff of Journal of Physics: Conference Series for their time and outstanding publishing work.The Committee of SEEIoT 2023List of Committee Member are available in this pdf.

Multi-objective Collaborative Planning Method for Micro-energy Systems Considering Thermoelectric Coupling Clusters

As the continuous development of integrated energy system and distributed power supply, operation economy and regional energy Internet reliability, especially micro-energy system, are increasing. Therefore, it is necessary to build multi-energy complementary micro-energy system, innovate energy supply mode, realize collaborative and efficient utilization among multi-energy systems, improve energy utilization efficiency and absorb renewable energy. In this paper, the decision model of distribution network planning scheme including distributed generator supply is established from four aspects: investment and operation cost, extra reserve capacity, energy conservation, reliability cost. The decision model involves a lot of parameter calculation and selection judgment, so after the decision goal is determined, an decision framework based on DS-MAS is established, that is, parameters are automatically calculated and selected based on different scenarios. Model validity is proved via a practical decision project.

Bias-Free Predictive Control of Power Converters with LCL Filter in Micro-Energy Systems

Power converters with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LCL</i> filters have been widely adopted in systems, e.g., micro-energy systems, grid-tied renewable energy systems, etc. Such a system forms a high-order <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multiple input and multiple output</i> (MIMO) dynamic system, requiring complicated active damping and showing slow control dynamics, applying classical cascaded linear controllers. Model predictive control (MPC) is a powerful control strategy, which inherently suits for MIMO systems with constraints. However, for such systems, inevitable tracking biases are seen when using the classical MPC. In addition, the nonlinear nature of the underlying system leads to difficulty for a deep analysis of an MPC controller design. In this work, we mathematically reveal the cause of tracking biases when applying classical MPC, and develop an equivalent modeling method to eliminate it at both parameter and model uncertainties, forming a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">robust and bias-free MPC</i> . The proposed solution remains fast in control dynamics and simple in structure. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Both</i> simulation <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">and</i> experimental data confirm the effectiveness of the proposed solution in mitigating tracking bias and good robustness at model deviations and grid disturbances.

ADAPTIVE CONTROL ELECTRICITY CONSUMPTION IN LOCAL MICROENERGY SYSTEM WITH POLYGENERATION BASED ON CLUSTERING OF A CONDITIONAL DYNAMIC TARIFF

The paper is devoted the increasing the efficiency to use of traditional and renewable sources in microenergy systems. The development of combined power supply systems of local objects with the stochastic nature generation of renewable sources has led to a number of fundamental problems.&#x0D; It is associated with the lack of a decision-making system regarding the ratio of power sources of electricity and the coordination of their generation levels with the electricity consumption schedule at the stage of designing such microgrids. The development of microenergy systems with polygeneration is conditioned by the possibility of technical and information support for the formation of the energy balance of local objects to minimize the costs of their electricity supply.&#x0D; By applying elements of inductive system-analytical technologies, an approach to solving the problem of modeling dynamic scenarios of energy management in local microgrids with polygeneration based on determining the cost of electricity and adaptive coordination of the electricity consumption schedule in real time is proposed.&#x0D; On the basis of statistical data, modeling was carried out and daily graphs of changes in the current cost of electricity with half-hour discreteness of the calculation were obtained, and the clustering procedure of the conditional dynamic tariff was showed.&#x0D; The results can be applied in the evaluation of electricity pricing in microgrid energy management systems or the development of control systems for Smart grids with renewable sources.

A multi-energy microgrid configuration method in remote rural areas considering the condition value at risk

The uncertainty caused by the growing use of renewable energy sources, such as wind and solar energy, makes it difficult to forecast the operation costs of micro-energy systems, particularly those in remote rural areas. Motivated by this point, this paper analyzes the possible operational risks and then introduces Condition Value at Risk (CVaR) to quantify the cost of the operational risk. On this basis, stochastic programming based on a multi-energy microgrid planning model that minimizes the investment cost, the operating cost, and the cost of operational risk, while considering the physical limitations of the multi-energy microgrid, is presented. Especially, scenarios of wind and solar energy output are generated using the Latin hypercube sampling method and reduced using the crowding measure-based scenario reduction method. After piecewise linearization and second-order cone relaxation, the model proposed in this paper is processed as a mixed integer linear model and solved by CPLEX. According to the achieved typical scenarios processed by the reduction method, the simulation shows that the presented configuration model can balance the investment cost and the cost of the operational risk, which effectively enhances the system’s ability to cope with uncertainties and fluctuations. Moreover, by adjusting the risk preference coefficient, the conservativeness of the planning scheme can be correspondingly adjusted.

Optimal configuration planning of vehicle sharing station-based electro-hydrogen micro-energy systems for transportation decarbonization

With the popularization of electric vehicles and hydrogen fuel vehicles, establishing an eco-friendly and economic-affordable fuel supply system turns out to be critical for the decarbonization of transportation sector. To achieve this, this paper proposes a systematic methodological framework to facilitate optimal configuration planning of an electric-hydrogen micro-energy system with vehicle sharing stations (EHVS). The proposed approach is based on the notion of integrated resource planning, which combines supply side with demand side simultaneously. The problem is formulated as a two-stage hybrid stochastic-interval programming, where the first-stage is aimed at minimizing the investment cost by determining the optimal configuration planning, while the second-stage is aimed at maximizing the expected benefits through optimal operation simulation of the system. The potential flexibility of vehicular demands in response to the rental price-setting has been explicitly considered in this work and represented using an evolutionary game model. Through this, the substitutability between different vehicle demands and its impact on the long-term planning of electric-hydrogen integrated energy system can be properly captured. A hybrid interval-stochastic method is presented to handle different types of uncertainties involved in the discussed problem, wherein the scenario analysis, the interval order relation and the fuzzy preference methods are jointly utilized. The resultant problem is solved with a genetic-algorithm based framework, and implemented in the MATLAB environment. The simulation results show that, compared with conventional independent planning, there could be an improvement in the carbon emission reduction by 24.74% and an additional cost saving of 11.97% achievable using the proposed approach.

Forecasting the electricity pricing of energy islands with renewable sources

The strategy for the development of local low-power systems involves the use of several sources. The efficiency of functioning such systems depends on the purposeful reliability management and it is based on the rational hierarchical connections of their structural components. Coordination of the structure of diversified sources and their participation in the formation of energy balance of micro-energy systems in the conditions of dynamic development of renewable energy is an actual research task. The purpose of research was to develop a method of reliability-cost optimization of structure of micro-energy systems with dissimilar sources, which is based on the use of reliability indicators and cost of electricity. The studies conducted are based on the modern methods of applied statistical analysis, the theory of reliability, the synthesis of complex multi-aggregate systems. Through the implementation of the Markov model and simulation modeling of the functioning of sources, it has been obtained the conditions for optimal formation of the energy balance of micro-energy system with the lowest cost of electricity, considering the reliability indicators. Computational experiments made it possible to obtain the regularities of cost evolution of electricity and to show its dependence on the structure and algorithms of the sources’ functioning. Using a probabilistic modeling method, it has been proved for the first time that the cost of electricity is sensitive to the ratio availability of renewable sources of primary energy. The practical application of results lies in the increase in efficiency of energy islands through the structural and algorithmic optimization of diversified sources (traditional and renewable) based on determining the cost of electricity

Relevance of channel shape in air-breathing membraneless direct formic acid microfluidic fuel cell: Experimental and numerical investigations

A 3-D model is established by considering mass transport phenomena, electrochemical reactions, electrode porosity, air breathing at the cathode, electrodes properties, fluid density and fluid viscosity. The model is applied to μFFC V- and U-shaped microfluidic channels. The agreement between the experiments and simulation in terms of predicted polarization curves, power outputs and fuel utilization indicate that the model can provide a trustworthy platform to study a wide set of stream architectures under numerous operating conditions. The power output and fuel efficiency utilization were improved by reducing the gap between the anode and the cathode. An optimum performance was achieved with a μFFC-U using 0.5 M HCOOH and flow rate of 100 μL min−1 delivering current density and power density output as high as 985 mA cm−2 and 165 mW cm−2, respectively. A proof of concept is demonstrated with a stack of three μFFC-Us (9.6 cm2 footprint and total volume of 10.6 cm3) connected in series powering four green LEDs (each of requiring a 2.1–2.5 V and 4.2–5 mW) for 20 h with low flow rate of 16.7 μL min−1. These results represent an important step towards the construction of microenergy systems for low power electronics applications.

Combustion Performance of Methane/Air in a Micro Combustor Embedded Hollow Hemispherical Slotted Bluff Body

With the rapid development of micro-energy power systems, the performance of micro-combustors as key components is in urgent need of further improvement. Aimed at enhancing combustion performance, a hollow hemispherical bluff body was used to analyze the methane combustion process. In this paper, we exploited the detailed reaction mechanism of methane/air with a laminar finite-rate model; the numerical analysis of methane combustion in the micro-combustor was carried out by Ansys Fluent software. The combustion, flow and thermal characteristics of the micro-combustor embedded with a hollow hemisphere bluff body (MCEHB) and the micro combustor embedded with a slotted hollow hemisphere bluff body (MCESHB) are compared, and the effect of slot width ratio on the combustion characteristics and thermal performance is discussed in detail. The results showed that the bluff body slotting treatment is not only beneficial to improving the velocity and temperature distribution behind the bluff body but also can improve the conversion rate of methane, especially at high inlet velocities. However, the conversion rate of methane is also affected by the slot width. When the slot width ratio below 0.5, the slot width corresponding to the peak methane conversion increased with the inlet velocity. Moreover, the bluff body slotting treatment can improve the wall temperature distribution, meanwhile expanding the high temperature area at the inner wall, thereby reducing the wall temperature fluctuation in the rear part of the micro-combustor. In addition, the optimal slot width ratio B increases with the inlet velocity. Since the inlet velocity is lower than 0.5 m/s, the optimal slot width ratio B is in the range of 0.3–0.375. However, as the inlet velocity exceeds 0.5 m/s, the optimal slot width ratio B moves to the range of 0.375–0.553. Furthermore, both large and small slot widths bring obvious temperature fluctuations to the micro combustor; the uneven wall temperature distribution phenomenon is detrimental to working performance. Therefore, the slot width ratio B of 0.375 only brings slight temperature fluctuations, indicating this is an optimal slot width ratio that should be chosen. This work has reference value for optimizing the design of the bluff body structure and improving the combustion performance of methane in the micro combustor.

Advanced Forecasting Methods of 5-Minute Power Generation in a PV System for Microgrid Operation Control

This paper concerns very-short-term (5-Minute) forecasting of photovoltaic power generation. Developing the methods useful for this type of forecast is the main aim of this study. We prepared a comprehensive study based on fragmentary time series, including 4 full days, of 5 min power generation. This problem is particularly important to microgrids’ operation control, i.e., for the proper operation of small energy micro-systems. The forecasting of power generation by renewable energy sources on a very-short-term horizon, including PV systems, is very important, especially in the island mode of microgrids’ operation. Inaccurate forecasts can lead to the improper operation of microgrids or increasing costs/decreasing profits for microgrid operators. This paper presents a short description of the performance of photovoltaic systems, particularly the main environmental parameters, and a very detailed statistical analysis of data collected from four sample time series of power generation in an existing PV system, which was located on the roof of a building. Different forecasting methods, which can be employed for this type of forecast, and the choice of proper input data in these methods were the subject of special attention in this paper. Ten various prognostic methods (including hybrid and team methods) were tested. A new, proprietary forecasting method—a hybrid method using three independent MLP-type neural networks—was a unique technique devised by the authors of this paper. The forecasts achieved with the use of various methods are presented and discussed in detail. Additionally, a qualitative analysis of the forecasts, achieved using different measures of quality, was performed. Some of the presented prognostic models are, in our opinion, promising tools for practical use, e.g., for operation control in low-voltage microgrids. The most favorable forecasting methods for various sets of input variables were indicated, and practical conclusions regarding the problem under study were formulated. Thanks to the analysis of the utility of different forecasting methods for four analyzed, separate time series, the reliability of conclusions related to the recommended methods was significantly increased.

Micro Energy Storage Systems in Energy Harvesting Applications: Analytical Evaluation towards Future Research Improvement.

During the last decade, countless advancements have been made in the field of micro-energy storage systems (MESS) and ambient energy harvesting (EH) shows great potential for research and future improvement. A detailed historical overview with analysis, in the research area of MESS as a form of ambient EH, is presented in this study. The top-cited articles in the field of MESS ambient EH were selected from the Scopus database, and based on articles published from 2010 to 2021, and the number of citations. The search for these top-cited articles was conducted in the third week of December 2021. Mostly the manuscripts were technical and contained an experimental setup with algorithm development (65%), whereas 27.23% of the articles were survey-based. One important observation was that the top 20 selected articles, which are the most-cited articles in the different journals, come from numerous countries of origin. This study revealed that the MESS integrated renewable energy sources (RESs) are an enhancement field of research for EH applications. On the basis of this survey, we hope to identify and solve research problems in the field of MESS and RESs integration, and provide suggestions for future developments for EH applications.

Flow Boiling and Heat Transfer of N-heptane Flow in a Microtube Heated by Concurrent Microflame

ABSTRACT Micro-diffusion flames have received much research attention due to their use in micro-energy systems. Experiments are performed with liquid heptane fuel at room temperature to assess the effects of fuel flow rates on micro-diffusion flame characteristics, wall temperature, and evaporation phenomena. At low fuel flow rate (≤10 µl/min), a steady, laminar flame with a spherical shape is formed, the maximum wall temperature recorded was 1050 K, a stable flow regime was observed with a dynamic meniscus, and the liquid fuel evaporated completely at the interface. At a medium fuel flow rate (30 µl/min), an unsteady flame with periodic patterns was formed, the maximum wall temperature dropped to 970 K, and the flow regime became unstable with liquid droplets in vapor regime. At a high fuel flow rate (≥50 µl/min), an explosive flame with an erratic pattern was obtained, the maximum wall temperature substantially decreases to 890 K, and an explosive boiling regime prevails with nucleate bubbles in the downstream region. The flame instability relies on meniscus oscillation and liquid droplets variation at low and high fuel flow rate, respectively.