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A two-step numerical concept was developed for modelling combustion and predicting nitrogen oxide emissions. The model was validated by the Sandia flame D experiment and with measurement data from burners on industrial furnaces. In this paper, the developed model was implemented to evaluate the influence of hydrogen blending with natural gas up to 40 vol.% on an industrial burner with oxidizer temperatures at 300 K and 813 K to assess the performance of the burner without altering the power output of the burner. An experimental test facility is under construction, and the feasibility of using this industrial burner on the test facility with different fuel mixtures was analyzed. Temperature, flow field, and emission characteristics were investigated. Using 40 vol.% hydrogen with natural gas resulted in a decrease of 14.82% in CO2 emissions and an increase of in 16.1% NO emissions when combusted with air at 300 K. The temperature profile indicated that the burner produces a symmetrical flame profile with preheated air and an asymmetrical flame profile with ambient air.

Ethanol is conventionally used as a blend with gasoline due to its similar properties, especially the octane number. However, ethanol has also been explored and used as a diesel substitute. While a low-blend of ethanol with diesel is possible with use of an emulsifier additive, a high-blend of ethanol with diesel may require major adjustment of compression-ignition (CI) diesel engines. Since dedicated CI engines are commercially available for a high-blend ethanol in diesel (ED95), a fuel mixture comprised of 95% ethanol and 5% additive, this technology offers an option for an oil-importing country like Thailand to reduce its fossil import by use of its own indigenous bio-ethanol fuel. Among many strong campaigns on ethanol utilization in the transportation sector under Thailand’s Alternative Energy Strategic Plan (2008–2022), the Thai Ministry of Energy has, for the first time, conducted a demonstration project with ethanol (ED95) buses on the Thai road system. The current investigation thus aims to assess and quantify the impact of using this ED95 technology to reduce fossil diesel consumption by adjusting the commercially available energy demand model called the Long range Energy Alternatives Planning system (LEAP). For this purpose, first, the necessary statistical data in the Thai transportation sector were gathered and analyzed to construct the predicative energy demand model. Then, scenario analyses were conducted to assess the benefit of ED95 technology on the basis of energy efficiency and greenhouse gas emission reduction.

Environmental sustainability remains a critical challenge in the face of global economic development. This study explored the complex interactions among renewable energy consumption, urbanization, trade openness, and economic development, focusing on their effects on environmental quality in 34 high-income European and Asian economies from 1970 to 2022. Using linear Bayesian regression and the Vector Error Correction Model (VECM), the analysis examined short- and long-term impacts to uncover nuanced relationships. Results demonstrated that economic development contributed to environmental degradation over the long term while mitigating it in the short term. Renewable energy consumption supported economic growth but showed limited efficacy in reducing ecological footprints across different time frames. Urbanization and trade openness emerged as significant drivers of long-term environmental degradation, emphasizing the need for targeted policy interventions. This study examined the link among economic progress and environmental sustainability, and identified key areas for improvement in urban planning, renewable energy, and trade policies. The findings provide a framework for policymakers to balance development with environmental preservation.

Recently, deep learning techniques have become popular and are widely employed in several research areas, such as optimization, pattern recognition, object identification, and forecasting, due to the advanced development of computer programming technologies. A significant number of renewable energy sources (RESs) as environmentally friendly sources, especially solar photovoltaic (PV) sources, have been integrated into modern power systems. However, the PV source is highly fluctuating and difficult to predict accurately for short-term PV output power generation, leading to ineffective system planning and affecting energy security. Compared to conventional predictive approaches, such as linear regression, predictive-based deep learning methods are promising in predicting short-term PV power generation with high accuracy. This paper investigates the performance of several well-known deep learning techniques to forecast short-term PV power generation in the real-site floating PV power plant of 1.5 MWp capacity at Suranaree University of Technology Hospital, Thailand. The considered deep learning techniques include single models (RNN, CNN, LSTM, GRU, BiLSTM, and BiGRU) and hybrid models (CNN-LSTM, CNN-BiLSTM, CNN-GRU, and CNN-BiGRU). Five-minute resolution data from the real floating PV power plant is used to train and test the deep learning models. Accuracy indices of MAE, MAPE, and RMSE are applied to quantify errors between actual and forecasted values obtained from the different deep learning techniques. The obtained results show that, with the same training dataset, the performance of the deep learning models differs when testing under different weather conditions and time horizons. The CNN-BiGRU model offers the best performance for one-day PV forecasting, while the BiLSTM model is the most preferable for one-week PV forecasting.

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In 2015, the National Energy Policy Council (NEPC) approved the latest Alternative Energy Development Plan (AEDP) 2015–2036, targeting electricity generation from biomass, biogas, and municipal solid waste by 2036 towards the Thailand 4.0 policy. The small biomass power plants are intensively promoted, contributing to many more public concerns. Therefore, this study provided new insight using the readiness and resilience in the communities near the biomass power plant generation in Southern Thailand. The community readiness model (CRM) and community health impact assessment (CHIA) were adopted using mixed methods during January–November 2019. A total of 999 respondents replied to the questionnaires, 153 informants were interviewed, and the panel was discussed and analyzed by descriptive statistics and content analysis. Findings illustrated that all stakeholder sectors strengthened community-driven development based on the average community readiness (3.01 ± 0.11) in a vague awareness stage, only with participation in information giving (75.38%) and having an impact pain point score of 7.64 ± 0.54, which was a highly intense level used to develop the public policy towards biomass power plants. Recent advanced community tools offered new insights for the first time about community strategic plans for sustainable biomass power generation, to achieve community security and values of democracy in Southern Thailand.

To realise the energy transition, every renewable source shall at least partially contribute to the demand–supply balancing, including customer-owned controllable loads and energy sources. Their commonly small size and spatial occurrence suggests addressing volatility issues locally, using local flexibilities to mitigate their impact. This calls for simple and effective signalling that enables interaction among local stakeholders, including local producers and customers. According interfaces and information formats appear to not yet exist. In this article, we propose a traffic-light-like system that enables the local grid operator to trigger situation-aware customer behaviour, supporting grid stability when needed and, in return, allowing customers to fully exploit temporary grid capacity when no safety or stability issues persist. The applied intuitive deduction method based on existing coordination mechanisms and objectives indicates, without proof, that the proposed granular traffic light system can enable the distribution grid flexibility required to facilitate more renewable energy being produced and inserted by local customers, to relieve grid levels above from transporting and equalising volatile energy shares, and to improve the economics of distributed renewable energy sources.

An electric multiple unit (EMU) high-speed train is the dynamic load that degrades the power quality in an electric railway system. Therefore, a power quality improvement system using an active power filter (APF) must be considered. Due to the oscillating load current in the dynamic load condition, a fast and accurate harmonic current-tracking performance is required. As such, this paper proposes the design of a model predictive control (MPC) since the minimization of cost function in the MPC process can suitably determine APF switching states. The design technique of MPC is based on the APF mathematical model. This controller was designed to compensate the time delay in the digital control. Moreover, the synchronous detection (SD) method applied the reference current calculations, as shown in this paper. To verify the proposed MPC, the overall control of APF was implemented on a eZdsp F28335 board by using the hardware-in-the-loop technique. The testing results indicated that the proposed MPC can provide a fast and accurate harmonic current-tracking response compared with the proportional-integral controller. In the load changing condition, the MPC was still effective in providing a good result after compensation. The percentage of total harmonic distortion, the percentage current unbalance factor, and the power factor would also be kept within the IEEE Standard 519 and IEEE Standard 1459.