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Carbon geological storage (CGS) is one of the key processes in carbon capture and storage (CCS) technologies, which are used to reduce CO2 emissions and achieve carbon-neutrality and net-zero emissions in developing countries. In Thailand, the Mae Moh basin is a potential site for implementing CGS due to the presence of a structural trap that can seal the CO2 storage formation. However, the cost of CGS projects needs to be subsidized by selling carbon credits in order to reach the project breakeven. Therefore, this paper estimates the economic components of a CGS project in the Mae Moh basin by designing the well completion and operating parameters for CO2 injection. The capital costs and operating costs of the process components were calculated, and the minimum carbon credit cost required to cover the total costs of the CGS project was determined. The results indicate that the designed system proposes an operating gas injection rate of 1.454 MMscf/day, which is equivalent to 29,530 tCO2e per year per well. Additionally, the minimum carbon credit cost was estimated to be USD 70.77 per tCO2e in order to achieve breakeven for the best case CGS project, which was found to be much higher than the current market price of carbon credit in Thailand, at around USD 3.5 per tCO2e. To enhance the economic prospects of this area, it is imperative to promote a policy of improving the cost of carbon credit for CGS projects in Thailand. [ABSTRACT FROM AUTHOR]

The aging process of transmission lines has a direct impact on the reliability and safety of the power grid. Therefore, an accurate age estimation method is imperative for effective maintenance planning and infrastructure investment. This paper introduces a systematic methodology for estimating the age of overhead transmission lines, utilizing the percentage statistical health index (%SHI) and the failure probability curve-fitting (FPCF) method. The %SHI, employing a scoring and weighting approach derived from test results and inspections, is used to assess the actual condition of transmission line equipment. Additionally, the FPCF approach is applied to illustrate the connection between the SHI and the likelihood of failure, facilitating the assessment of transmission line age by fitting failure probability curves to the SHI data. This age is directly associated with the probability of experiencing a failure. The evaluation was conducted on 924 towers situated along four transmission lines connecting the 115 kV substations S1–S2, S3–S4, S5–S6, and S7–S8. These transmission lines are in four regions with diverse terrain and environments such as mountains, rice fields, and more. In the SHI calculation, practical testing results and historical failure data were applied. The results clearly indicate that there were notable disparities in the age estimations for transmission lines in diverse geographical regions of Thailand when compared to their actual ages. These discrepancies can be attributed to various factors, including the local environment, such as rainfall, flooding, and salt-laden air as well as specific geographical features like mountainous and coastal terrain. To mitigate the deterioration of transmission lines in all regions, it is essential to implement a proactive maintenance strategy. This strategy should involve more frequent inspections, the use of advanced monitoring technologies, and the establishment of robust maintenance procedures, with which it would become possible to enhance the accuracy of equipment condition assessments, ultimately resulting in an overall improvement in the reliability of transmission lines. [ABSTRACT FROM AUTHOR]

Accurate electricity demand forecasting is essential for global energy security, reducing costs, ensuring grid stability, and informing decision making in the energy sector. Disruptions often lead to unpredictable demand shifts, posing greater challenges for short-term load forecasting. Understanding electricity demand patterns during a pandemic offers insights into handling future disruptions. This study aims to develop an effective forecasting model for daily electricity peak demand, which is crucial for managing potential disruptions. This paper proposed a hybrid approach to address scenarios involving both government intervention and non-intervention, utilizing integration methods such as stepwise regression, similar day selection-based day type criterion, variational mode decomposition, empirical mode decomposition, fast Fourier transform, and neural networks with grid search optimization for the problem. The electricity peak load data in Thailand during the year of the COVID-19 situation is used as a case study to demonstrate the effectiveness of the approach. To enhance the flexibility and adaptability of the approach, the new criterion of separating datasets and the new criterion of similar day selection are proposed to perform one-day-ahead forecasting with rolling datasets. Computational analysis confirms the method's effectiveness, adaptability, reduced input, and computational efficiency, rendering it a practical choice for daily electricity peak demand forecasting, especially in disrupted situations. [ABSTRACT FROM AUTHOR]

Rising fossil energy prices and the significantly decreasing prices of energy technology have resulted in electricity consumers having the option to install solar PV rooftops to rely on the self-consumption of clean energy. However, the increase in this amount is affecting the revenue of electricity as a utility, which must adapt and develop its business model to accommodate the situation. If the utility cannot be adapted in time, it may lead to a loss of income from services and the sale of electricity from fossil energy. The utility in Thailand's electricity market that acts as the distribution system operator (DSO) is known as the Provincial Electricity Authority (PEA), and the Metropolitan Electricity Authority (MEA) is responsible for managing distribution networks and customers. There are four types of solar rooftop-as-a-service (RaaS) business perspectives they could consider as opportunities through which to minimize revenue impact. The business services were designed for the DSO customer as follows: Consulting, Design, and Installation (CDI); Operation and Maintenance (O&M); Energy Service Company (ESCO); and Power Purchase Agreement (PPA). The model comprises four customer segments: residential buildings and small-, medium-, and large-scale commercial buildings. This paper applies SWOT, Five Forces, 4P marketing, and economic impact analyses to identify the possibilities when using the DSO business model. The SWOT analysis demonstrates that ESCO and PPA are strengths in the DSO's performance characteristics and existing customer data. In the electricity industry, both models offer enormous customer bargaining power in terms of a Five Forces analysis. The main reason is that there is currently high competition in the installation service. In the 4P analysis result, the price per unit is found to be significantly lower than in residential scenarios. Therefore, there is a format for presenting promotions with an advantage over competitors. Deploying an after-sales service that brings convenience to all customer segments is needed. The economic analysis conducted using Cournot competition game theory shows a significant differential in the Medium (M) and Large (L) customer sectors' competition due to lower technology prices. In conclusion, with the current regulatory framework and criteria, the ESCO and PPA show the best practical model from a utility business perspective. The recommendation for DSO is to create a strategic ecosystem and to link it with private companies as their partnership business. [ABSTRACT FROM AUTHOR]

The growth of microgrids to fulfil electricity demand could lead to arguments or conflicts between the private microgrid system owners and the existing national distribution authority, especially in overlapping sales areas. The core issue identified in this paper is a distribution system management model based on the cooperative concept of unifying the multi-owned networks as a single system to ensure the natural monopoly function. This model can avoid complex wheeling charges across multi-owned grids within overlapping sales areas, in which all network users pay at the expense of the merged system charge. This paper proposes the model and shows numerical examples using a suitable distribution network pricing model to recover existing costs and plan for future system expansion. The proposed model and charging algorithm were tested on a modified IEEE 13 bus to examine their impacts. The result demonstrates that the model resolves the issues with the overlapping sales area and creates a fair scenario for the network users regarding the usage charges. Thailand is chosen as the primary reference area, as it is a country with an actual sample case for the argument. [ABSTRACT FROM AUTHOR]

This paper presents a fuzzy logic approach, a simplified and adaptable method, to determine the health index of gas-insulated switchgear (GIS) bay and their compartments. Since the traditional weighting and scoring method (WSM) is a subjective method, the fuzzy logic approach has been applied to enhance the accuracy of the health index (HI) determination by evaluating the detectable degradation and the incorporated conditional factor (CF) considering actual operating conditions and invisible ageing. The input data are first obtained from routine inspection and time-based testing and then converted to numerical values as the fuzzy logic model input to compute the component HI. The bay HI values are further calculated by applying the WSM using the obtained component of the HI. Then, the accuracy of the obtained HI of the bay has been improved by multiplying with the CF to obtain the overall bay HI values. The proposed methodology was implemented in an independent power producer in a large industrial estate in Thailand to evaluate 175 GIS bays with actual data. The results were compared against other HI evaluation techniques with a satisfactory outcome. Finally, the overall bay HI is used to prioritize maintenance activity, to effectively allocate human resources, to prevent unplanned outages, and to achieve cost-effective, condition-based maintenance. [ABSTRACT FROM AUTHOR]

Plug-in electric vehicles (PEVs) and distributed generation (DG) can positively and negatively impact the distribution system. Therefore, this paper presents the modeling and analysis of DG and PEVs' penetration levels of the three-phase unbalanced radial distribution system. The study aims to optimize the distribution system's DG sizing and PEV charging to minimize total power loss. The test system is the 4th circuit of the Nonsung service station along Thaharn Road, Muang District, Udon Thani, Thailand. According to objective function and constraints, such control variables as installation buses and rated outputs of DG and the penetration levels of PEVs were obtained to evaluate the total power loss. Significantly, the charging demand of PEVs is an uncertain load estimated by queuing theory integration with the minimization tool called the differential evaluation (DE) method. According to the result comparison of a four case simulation, the total power losses of queuing theory and DE application are minimum. Finally, total power losses conform to the regulation of the Provincial Electricity Authority (PEA), Thailand. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Daily electricity peak load forecasting is important for electricity generation capacity planning. Accurate forecasting leads to saving on excessive electricity generating capacity, while maintaining the stability of the power system. The main challenging tasks in this research field include improving forecasting accuracy and reducing computational time. This paper proposes a hybrid model involving variational mode decomposition (VMD), empirical mode decomposition (EMD), fast Fourier transform (FFT), stepwise regression, similar days selection (SD) method, and artificial neural network (ANN) for daily electricity peak load forecasting. Stepwise regression and similar days selection method are used for input variable selection. VMD and FFT are applied for data decomposition and seasonality capturing, while EMD is employed for determining an appropriate decomposition level for VMD. The hybrid model is constructed to effectively forecast special holidays, which have different patterns from other normal weekdays and weekends. The performance of the hybrid model is tested with real electricity peak load data provided by the Electricity Generating Authority of Thailand, the leading power utility state enterprise under the Ministry of Energy. Experimental results show that the hybrid model gives the best performance while saving computation time by solving the problems in input variable selection, data decomposition, and imbalance data of normal and special days in the training process. [ABSTRACT FROM AUTHOR]

Solar PhotoVoltaics (PV) integration into the electricity grids significantly increases the complexity of Transmission Expansion Planning (TEP) because solar PV power generation is uncertain and difficult to predict. Therefore, this paper proposes the optimal planning method for transmission expansion combined with uncertain solar PV generation. The problem of uncertain solar PV generation is solved by using Long Short-Term Memory (LSTM) for forecasting solar radiation with high accuracy. The objective function is to minimize total system cost, including the investment cost of new transmission lines and the operating cost of power generation. The optimal TEP problem is solved by the Binary Differential Evolution (BDE) algorithm. To investigate and demonstrate the performance of the proposed method, the IEEE 24-bus system and solar radiation data in Thailand are selected as a study case for TEP. The MATPOWER program written in MATLAB software is used for solving optimal power flow problems. Simulation results show that the proposed optimal TEP method combined with forecasting solar PV power generation using the LSTM can reduce the total system cost of the transmission expansion by 9.12% compared with the cost obtained by the TEP using solar radiation from statistical data. [ABSTRACT FROM AUTHOR]

In this paper, a new D-distance factor is proposed to determine the failure probability and to prioritize maintenance actions of power circuit breakers in high-voltage substations. The D-distance factor is calculated by using the condition index and renovation index of a high-voltage circuit breaker (HVCB). To facilitate effective decision-making on maintenance with a simple method and less computational effort, the proposed model incorporates the weighting–scoring method (WSM) and analytical hierarchy process (AHP) with the various diagnostic methods for condition index assessments as well as the operation requirements of HVCBs for renovation index assessments. Many significant parameters from circuit breaker testing, such as insulation resistance, contact resistance, contact timing, SF6 gas measurements, gas leakage rate, visual inspection, etc., have been considered for condition index determination. In addition to these, other significant parameters, such as age of the circuit breaker, age of the interrupter and mechanism, number of fault current interruptions, actual load current to rated current ratio, actual short circuit current to rated interrupting current ratio, maintenance ability, spare parts availability, maintenance expertise level, etc., are also considered for renovation index determination. To validate the proposed model, the practical test data of twenty 115 kV HVCBs in various substations of a distribution utility in Thailand were utilized and tested. By analyzing the actual condition and operation requirement of the circuit breaker, the output, as the condition index and renovation index using the proposed method, is discussed with HVCB experts in the utility to adjust the scores and weights of all criteria to obtain the most accurate and reliable model. The results show that the D-distance technique measured from the risk matrix, which is defined as the failure probability, can be used to rank the maintenance schedule from urgent to normal maintenance tasks. In addition, various failure probabilities in the risk matrix of the circuit breaker can be used to determine the appropriate maintenance strategies for the power circuit breaker in each group. Finally, the proposed method could help the utility managers and maintenance engineers manage the maintenance planning effectively and easily for thousands of HVCBs in the grid, and it can be further applied with other high-voltage equipment in both transmission and distribution systems to facilitate the maintenance activities according to available costs and human resources. [ABSTRACT FROM AUTHOR]

This paper proposes decarbonization pathways for Thailand based on a review of the status of renewable and fossil energies, technology evaluation and scenario studies. Results show that renewable electricity generation needs to grow at a 7.1% average annual growth rate (AAGR) between now and 2050 for the power sector to achieve net-zero by 2050. This would require it to reach 400 TWh, exceeding its technical potential. We propose a more achievable scenario of between 5% and 6% AAGR wherein renewable electricity will grow from 51 TWh to 217–291 TWh between 2020 and 2050. Gas-powered electricity will grow from 127 TWh to 185–111 TWh, requiring carbon capture and storage (CCS) to mitigate 75–45 Mtpa CO2 by 2050. For the transport sector, electric vehicles have the highest decarbonization potential, but they would add 45 TWh of electricity demand by 2050. For the industry sector, installing CCS in existing plants has the highest decarbonization potential. Overall, CCS is a key decarbonization technology and its large-scale implementation will be needed for Thailand to achieve net-zero by 2050. [ABSTRACT FROM AUTHOR]

This paper proposes a comprehensive procedure to assess the condition of gas insulated switchgear (GIS) equipment by using the conventional weight and score method and introducing a conditional factor to improve the accuracy of the health index evaluation. Generally, the inspection and testing of GIS components are conducted according to manufacturer recommendations and guidelines in the international standards. However, this raw data has not been simplified and systematically processed for condition assessment. The score and weight technique are applied to transform the physical condition according to visible and measurable aging to numerical values in terms of component and bay health index values. The accuracy of the obtained health index has been improved by a conditional factor, which considers invisible aging factors, such as age, number of switching operations, degree of satisfactory operation, obsolescence, and adequacy of the interrupting rating. Here, a condition evaluation procedure has been developed and compared with the fuzzy logic method and the health index dominant score technique with satisfactory results. Subsequently, the proposed procedure has been developed as web application software to evaluate 175 bays of GIS in both 115 and 230 kV networks of an independent power producer supplying electricity of 3094 MW to a large industrial estate in Thailand. Eight GIS bays showed moderate or poor condition and the proper actions were assigned to prevent their failure. The software is in use in practice as a decision support tool to effectively manage the maintenance tasks and to improve supply reliability. [ABSTRACT FROM AUTHOR]

In power system planning, the growth of renewable energy generation leads to several challenges including system reliability due to its intermittency and uncertainty. To quantify the relatively reliable capacity of this generation, capacity credit is usually adopted for long-term power system planning. This paper proposes an evaluation of the capacity credit of renewable energy generation using stochastic models for resource availability. Six renewable energy generation types including wind, solar PV, small hydro, biomass, biogas, and waste were considered. The proposed models are based on the stochastic process using the Wiener process and other probability distribution functions to explain the randomness of the intermittency. Moreover, for solar PV—the generation of which depends on two key random variables, namely irradiance and temperature—a copula function is used to model their joint probabilistic behavior. These proposed models are used to simulate power outputs of renewable energy generations and then determine the capacity credit which is defined as the capacity of conventional generation that can maintain a similar level of system reliability. The proposed method is tested with Thailand's power system and the results show that the capacity credit depends on the time of day and the size of installed capacity of the considered renewable energy generation. [ABSTRACT FROM AUTHOR]

To avoid the potential adverse impacts of climate change from global warming, it is suggested that the target of net zero emissions should be reached by this mid-century. Thailand is aiming to achieve carbon neutrality by 2050. Since electricity generation is one of the largest producers of carbon dioxide emission, the associated emissions must be greatly reduced to achieve the targets mentioned above. Thus, new generation expansion plans must be well developed. This paper discusses the development of generation expansion plans considering Thailand's latest policies along with enhancement of the existing multi-period linear programming model, allowing new electricity generation technologies having low emissions, e.g., solar PV with battery and hydrogen blending in natural gas, to be integrated into generation expansion planning. Then, four generation expansion plans with different levels of hydrogen blending in natural gas are proposed and discussed. It is found that Thailand can achieve carbon neutrality by 2050 by promoting more use of renewable energy altogether with trade-off between land for solar PV installation and amount of hydrogen blended in natural gas. The lesson learned from this study provides crucial information about possible pathways to achieve carbon neutrality in the electricity sector for policy makers in other countries. [ABSTRACT FROM AUTHOR]

Situated in the southern part and on the western coast of Thailand, the Andaman Coast covers the provinces of Ranong, Phangnga, Phuket, Krabi, Trang and Satun. Using a coupled mesoscale atmospheric model and a microscale wind flow model, along with computational fluid dynamics (CFD) modeling, this paper presents a detailed assessment of the wind energy potential for power generation along the Andaman Coast of Thailand. The climatic data are obtained from the Modern Era Retrospective analysis for Research and Applications (MERRA), along with a high-resolution topography database and Land Use Land Cover digital data. The results are compared to the equivalent wind speeds obtained with the Weather Research and Forecasting (WRF) atmospheric model. The results showed that, at 120 m above ground level (agl), the predicted wind speeds from the models proposed were 20% lower for the mesoscale model and 10% lower for the microscale model when compared to the equivalent wind speeds obtained from the WRF model. A CFD wind flow model was then used to investigate 3D wind fields at 120–125 m agl over five potential sites offering promising wind resources. The annual energy productions (AEP) and the capacity factors under three different wake loss models and for five wind turbine generator technologies were optimized for 10-MW wind power plants, as per Thailand's energy policies. With capacity factors ranging from 20 to 40%, it was found that the AEPs of the best sites were in the range of 18–36 GWh/year, with a total AEP in the vicinity of 135 GWh/year when using a single wind turbine model for the five sites studied. The combined energy productions by these wind power plants, once operational, could avoid GHG emissions of more than 80 ktons of CO2eq/year. [ABSTRACT FROM AUTHOR]

Air pollutants and greenhouse gases (GHGs) represent major challenges in our era, contributing to climate change and global health issues. These problems arise from a variety of well-known sources, including motor vehicles. Almost all nations, Thailand included, have formulated and implemented policies to curb greenhouse gas (GHG) emissions in line with the requirements and commitments of the Paris Agreement. The evaluation of specific air pollutants and GHG emissions originating from road vehicles utilises the Thailand database, referencing the year 2019. Data intersections from 2019 to 2022 are grounded in actual data collected from relevant departments in Thailand, while projections for 2023–2030 are forecasted based on the baseline year. The secondary database used in the International Vehicle Emission model is adjusted according to real-world driving data to accurately reflect country-specific emission factors. Dynamic emission factors for specific air pollutants and GHGs are evaluated and integrated with the average Vehicle Kilometres Travelled (VKT) for each vehicle category. The Business-As-Usual (BAU) scenario is then examined, based on existing policies aimed at reducing air pollutants and GHG emissions in Thailand's transport sector. These policies include strategies for the adoption of electric vehicles and the promotion of public transport to reduce VKT. Under the BAU scenario, the overall number of road vehicles in Thailand, including passenger cars, motorcycles, pickups, vans, trucks, and buses, is expected to increase by approximately 6.58% by 2030, leading to a rise in specific air pollutants and GHG emissions compared to the 2019 baseline. However, by adhering to Thailand's strategies and transitioning to new electric passenger cars and buses, greenhouse gas emissions and specific air pollutants from the road transport sector will be significantly reduced. [ABSTRACT FROM AUTHOR]

The state-owned power Electricity Generating Authority of Thailand (EGAT), a monopoly market in charge of producing, distributing, and wholesaling power, is the focal point of Thailand's electricity market. Although the government has encouraged people to install on-grid solar panels to sell electricity as producers and retail consumers, the price mechanism, i.e., purchasing price and selling prices, is still unilaterally determined by the government. Therefore, we are interested in studying the case where blockchain can be used as a free trading platform. Without involving buying or selling from the government, this research presents a model of fully traded price mechanisms. Based on the study results of the double auction system, data on buying and selling prices of electrical energy in Thailand were used as the initial data for the electricity peer-to-peer free-trading model. Then, information was obtained to analyze the trading price trends by using the law of demand and supply in addition to the principle of the bipartite graph. The price trend results agree well with those of price equilibrium equations. Therefore, we firmly believe that the model we offer can be traded in a closed system of free-trade platforms. In addition, the players in the system can help to determine the price trend that will occur according to various parameters and will cause true fairness in the sustainable electricity supply chain industry in Thailand. [ABSTRACT FROM AUTHOR]

This paper proposes a methodology to develop generation expansion plans considering energy storage systems (ESSs), individual generation unit characteristics, and full-year hourly power balance constraints. Generation expansion planning (GEP) is a complex optimization problem. To get a realistic plan with the lowest cost, acceptable system reliability, and satisfactory CO2 emissions for the coming decades, a complex multi-period mixed integer linear programming (MILP) model needs to be formulated and solved with individual unit characteristics along with hourly power balance constraints. This problem requires huge computational effort since there are thousands of possible scenarios with millions of variables in a single calculation. However, in this paper, instead of finding the globally optimal solutions of such MILPs directly, a simplification process is proposed, breaking it down into multiple LP subproblems, which are easier to solve. In each subproblem, constraints relating to renewable energy generation profiles, charge-discharge patterns of ESSs, and system reliability can be included. The proposed process is tested against Thailand's power development plan. The obtained solution is almost identical to that of the actual plan, but with less computational effort. The impacts of uncertainties as well as ESSs on GEP, e.g., system reliability, electricity cost, and CO2 emission, are also discussed. [ABSTRACT FROM AUTHOR]

Reclamation is regarded as one of the mining processes that can lessen the environmental impact of its production, particularly for large-scale coal mines that emit significant quantities of greenhouse gases. However, the assessment and evaluation of the reclamation process primarily rely on qualitative methods. Utilizing LANSAT8 Operational Land Imager (OLI) remote sensing in conjunction with GIS, this study aimed to develop a quantitative method for validating the efficacy of a reclamation procedure applicable to the emerging trend of carbon reduction. The empirical formula utilized to compute the annual carbon sequestrations of the reclamation area in the Mae Moh mine exhibited the appropriate spatial relative standard deviation (S-RSD) at 98.25%. The findings indicate that the reclamation area reached its highest level of carbon sequestration in 2022, at 331.28 ± 11.89 ktCO2e, surpassing the baseline of 126.53 ktCO2e. Furthermore, the approach demonstrates significant potential in improving the standard method for assessing reclamation through reforestation. [ABSTRACT FROM AUTHOR]

Justice and sustainability are the foundation of fair and equitable biofuel development. Policies and practices are consequently necessary to support a just transition towards a low-carbon economy. Therefore, this study aimed to understand multi-actor perspectives on policies and practices relevant to the just transition in biofuel development. Applying a socio-legal approach based on the JUST framework, this study focused on the Indonesian biofuel industry, which is primarily driven by crude palm oil (CPO) production. This study also added comparative perspectives from Thailand's biofuel industry. The primary data were gathered from in-depth interviews and focus group discussions (FGDs), while the secondary data were from research, policy, and other government archives. Based on the results, oil palm plantations in Indonesia and Thailand are crucial for macroeconomic development. However, smallholder farmers in Indonesia, especially independent ones that do not join farmer groups, remain marginalised by the current regulations. In fact, many benefits from biofuel practices and policies accumulate disproportionately towards large corporations, while marginalised groups bear the costs. The Indonesian Sustainable Palm Oil (ISPO) certification that should induce market accessibility and competitiveness has not fully reached most rural farmers. Furthermore, various overlapping regulations and perplexing data differences exist from multiple ministries and administrative levels. Thus, palm oil stakeholders in Indonesia demand a dedicated institution for integrated regulation and monitoring of the industry, similar to the National Palm Oil Policy Committee in Thailand. The myriad of legal, political, socio-economic, and justice issues thus necessitates all hands on deck to ensure a just transition for all stakeholders of the Indonesian biofuel industry. [ABSTRACT FROM AUTHOR]

The escalating concerns over climate change have propelled industries worldwide to seek innovative strategies for mitigating greenhouse gas emissions. Within the energy sector, Carbon Capture and Storage (CCS) technology emerges as a promising solution to curtail emissions and foster sustainable development aims for the net zero approach. This research delves into the role of government support in expediting CCS adoption for the maximum potential of 9.79 MtCO2 storage from six major refinery plants. The refineries mentioned above are anticipated to necessitate an initial capital investment of approximately 18,307 million THB. This research focuses on potential business model proposals appropriate for a country's context, specifically, applying CCS technology to the Thai oil refining sector. To achieve the realization of CCS within the context of this study, a combination of three essential measures will be required: tax incentives, carbon credits, and grants. This process will commence with the implementation of tax incentives, followed by an increase in the carbon price within the country. Finally, the establishment of a dedicated fund, funded through deductions from oil excise tax revenue, will play a pivotal role in facilitating the necessary financial support for the emergence of CCS. [ABSTRACT FROM AUTHOR]

In the present paper, an investigation into Thailand's energy demand is performed to determine if: (1) a linear or nonlinear Engel curve better explains the relationship between income and energy consumption, and (2) systems with pre-commitments better model energy consumptions. Four demand systems are estimated: an almost ideal demand system (AIDS), the quadratic almost ideal demand system (QAIDS), generalized almost ideal demand system (GAIDS), and the generalized quadratic almost ideal demand system (GQAIDS). Elasticities are calculated for policy implications. The empirical results suggest that models considering pre-commitments and nonlinear Engel curves may be slightly more appropriate for Thailand's energy system, from both statistic and economic standpoints. Statistical inferences appear to favor the GQAIDS model based on the encompassing results. Economic reasonability also appears to favor the GQAIDS model, in particular, petroleum products, as it provides results consistent with the notions of precommitments and fuel substitutability found in previous studies. Most of the previous studies in various forms have shown that the demand for petroleum products is relatively inelastic to price in Thailand. The current study, however, finds that own-price elasticities of uncompensated demand for petroleum products are almost unitary, which is relatively more elastic than most of the previous studies. As such, further studies are required and the price-based policy on petroleum products targeting the reduction in petroleum product dependence must be implemented with caution. [ABSTRACT FROM AUTHOR]

Recent developments in disruptive technologies along with the cost reduction of photovoltaics have been transforming business models in the electricity sector worldwide. The rise of prosumers has led to a more decentralized and open local green energy market through the emergence of peer-to-peer (P2P) energy trading, where consumers and prosumers can buy or sell electricity through an online trading platform. P2P energy trading has the potential to make green energy more accessible at the local level, provide a customer choice that aligns with community values, and promote the use of renewable energy (RE) for local consumption. Although P2P energy trading has already been adopted in some countries, its implementation remains challenging in other countries, including Thailand. In this work, we investigated the drivers and challenges of implementing P2P energy trading in Thailand based on the perspectives of P2P energy trading pilot project developers participating in the regulatory sandbox program. A strategic framework was used to identify the respondents' standpoints on the political, economic, social, technological, legal, and environmental (PESTLE) factors that can influence the implementation of P2P energy trading. This can help businesses, policymakers, and regulators better understand drivers and barriers of P2P energy trading, which is a potential local energy market. This paper also provides policy recommendations for regulatory changes for the future development of P2P energy trading, including opening a third-party access (TPA) regime, enabling a liberalized market in the electricity market, and integrating the role and responsibilities of the prosumer for P2P energy trading into existing law. [ABSTRACT FROM AUTHOR]

The demand response (DR) program is one of the most promising components in the development of the Smart Grid. However, there are many challenges in practical operation to improve the existing and outdated system to comply with the DR programs. In Thailand, the major pain point of the office building owner in the DR program is the additional equipment, modification and operation cost of the existing equipment. Moreover, the sophisticated solution and control are other obstacles that need more measurements and data, and they make the operation difficult to work with. In this paper, we implemented a simple yet cost-effective hardware and software solution targeting an outdated air-conditioning system without voiding the warranty of the outdated equipment and without installing any additional measurements. In addition, the proposed operation is designed to be easy to operate under the equipment limitation and unskilled labor. More importantly, indoor temperature setpoint schedules during the DR event are forecasted with some public datasets to determine the capacity of the energy management system that can reduce the power consumption in the office building without an effect on the occupants' comfort. To confirm the practicality of the proposed solution, the actual operation of the proposed solution can achieve the maximum power reduction at 19.80 kW (43.79% of the maximum power consumption) while keeping only 1 °C of difference from the typical room temperature (26–28 °C). [ABSTRACT FROM AUTHOR]

It is well documented that both solar photovoltaic (PV) systems and electric vehicles (EVs) positively impact the global environment. However, the integration of high PV resources into distribution networks creates new challenges because of the uncertainty of PV power generation. Additionally, high power consumption during many EV charging operations at a certain time of the day can be stressful for the distribution network. Stresses on the distribution network influence higher electricity tariffs, which negatively impact consumers. Therefore, a home energy management system is one of the solutions to control electricity consumption to reduce electrical energy costs. In this paper, a meta-heuristic-based optimization of a home energy management strategy is presented with the goal of electrical energy cost minimization for the consumer under the time-of-use (TOU) tariffs. The proposed strategy manages the operations of the plug-in electric vehicle (PEV) and the energy storage system (ESS) charging and discharging in a home. The meta-heuristic optimization, namely a genetic algorithm (GA), was applied to the home energy management strategy for minimizing the daily electrical energy cost for the consumer through optimal scheduling of ESS and PEV operations. To confirm the effectiveness of the proposed methodology, the load profile of a household in Udonthani, Thailand, and the TOU tariffs of the provincial electricity authority (PEA) of Thailand were applied in the simulation. The simulation results show that the proposed strategy with GA optimization provides the minimum daily or net electrical energy cost for the consumer. The daily electrical energy cost for the consumer is equal to 0.3847 USD when the methodology without GA optimization is used, whereas the electrical energy cost is equal to 0.3577 USD when the proposed methodology with GA optimization is used. Therefore, the proposed optimal home energy management strategy with GA optimization can decrease the daily electrical energy cost for the consumer up to 7.0185% compared to the electrical energy cost obtained from the methodology without GA optimization. [ABSTRACT FROM AUTHOR]

Electricity peak load forecasting plays an important role in electricity generation capacity planning to ensure reliable power supplies. To achieve high forecast accuracy, multiple machine learning models have been implemented to forecast the monthly peak load in Thailand over the past few years, yielding promising results. One approach to further improve forecast accuracy is to effectively select the most accurate forecast value for each period from among the forecast values generated by these models. This article presents a novel reinforcement learning approach using the double deep Q-network (Double DQN), which acts as a model selector from a pool of available models. The monthly electricity peak load data of Thailand from 2004 to 2017 are used to demonstrate the effectiveness of the proposed method. A hyperparameter tuning methodology using a fractional factorial design is implemented to significantly reduce the number of required experimental runs. The results indicate that the proposed selection model using Double DQN outperforms all tested individual machine learning models in terms of mean square error. [ABSTRACT FROM AUTHOR]

Among the energy targets of the Vietnamese government, solar energy is expected to become the main source of renewable energy in the future. Solar energy is moving forward, with Vietnam outstripping Thailand and becoming the country that installed the largest capacity of solar power generation in Southeast Asia, reaching 16,362 MW in new installations in December 2021. In this study, we have experimentally analyzed and designed a capacity of 47.5 MW grid-connected photovoltaic plant mounted on the floatation system at Da Mi hydropower reservoir in Binh Thuan province. This was selected to be utilized as the first effort to develop the first large-capacity floating solar power plant on a hydroelectric reservoir in Vietnam. A detailed examination of the electrical analysis, including DC to DC converters, AC inverters to the transmission network, and PV module connectivity configurations, are in scope. The present research has the potential to make a contribution to the design of the DC electrical part, the AC electrical part, and the layout PV modules—Inverter—Floatation system—Floating bridge of FPV plants which are less described in the former articles. The performance of the plant after the first 2 years of operation has confirmed that it has met the expectations and exceeded the investor's target, with the power output of the first 2 years being higher than the design by 102.58% to 105.59% and no serious damage has occurred to the equipment from 1 June 2019 to 31 August 2021. [ABSTRACT FROM AUTHOR]

In 2022, Thailand's Demand Response (DR) business model was shifting from the Traditional Utility (TU) model to the Load Aggregator (LA) model in accordance with Thailand's smart grid master plan. This research studied the current demand response model and mechanism to draw possible gaps in operations. This research deals with the data system owned by the individual load aggregator. The load aggregators collect meter data and evaluate demand adaptations before sending the results to claim compensation on behalf of their customers. This approach lacks transparency and facilitates distortion of the facts. Hence, this research introduces the data execution by smart contracts and data records on the blockchain that enhance transparent data sharing among multiple parties and maintain data integrity. Moreover, the proposed bidding algorithm allows customers to offer an expected price under the maximum incentive payment determined by the avoided costs of running the peaking power plants. Hence, the bidding helps reflect the DR operation costs on the customer side and control the budget for incentive payments. This study emphasized the smart contracts and decentralized application layer, so the public blockchain is a reasonable network for the test. However, implementation in real cases using the public blockchain requires careful considerations, such as network fees, transaction speeds, and the security of smart contract codes. [ABSTRACT FROM AUTHOR]

Biodrying is an essential part of the mechanical–biological treatment process that minimizes moisture content and simultaneously maximizes heating value for refuse-derived fuel (RDF) production. Although the mechanical separation process operates effectively in Thailand's RDF production, high organic content levels and their degradation cause moisture contamination in RDF, producing wet RDF. Aeration is essential for an effective biodrying process, and can reduce RDF's moisture content as well as increase its heating value. To maximize the biodrying effect, aeration should be optimized based on the waste conditions. This study proposes a modified aeration-supplied configuration for wet RDF biodrying. The aeration rate was modified based on the period within the biodrying operation; the first period is from the beginning until day 2.5, and the second period is from day 2.5 to day 5. The optimal aeration supply configuration was 0.5 m3/kg/day in the first period and then 0.3 m3/kg/day until the end of the process; this configuration yielded the greatest moisture content decrease of 35% and increased the low heating value of the biodried product by 11%. The final moisture content and low heating value were 24.07% and 4787 kcal/kg, respectively. Therefore, this optimal aeration-supplied configuration could be applied to meet the moisture content and low heating value requirements of the RDF production standard for Thailand's local cement industry. [ABSTRACT FROM AUTHOR]

Realizing urban energy systems with net-zero CO2 emissions by 2050 is a major goal of global societies in building sustainable and livable cities. Developing cities hold a key to meeting this goal, as they will expand rapidly in the next decades with increasing energy demand, potentially associated with rising CO2 emissions and air pollution if fossil fuels continue to be utilized. Therefore, identifying equitable, cost-effective, and deep decarbonization pathways for developing cities is essential. Here, we analyzed Bangkok City, Thailand, using the System Advisor Model (SAM) for techno-economic analysis to evaluate the decarbonization potential of rooftop photovoltaics (PV) integrated with electric vehicles (EVs) as batteries on a city scale. The analyses took into consideration hourly local weather conditions, electricity demand, electricity tariffs, feed-in-tariffs, degradation, declining costs of PV and EV, etc., specific to Bangkok. As the prices of PV and EVs decrease over the next several decades, the "PV + EV" system may provide a basis for new urban power infrastructure with high energy efficiency, low energy cost, and large CO2 emission reduction. The results show that the "PV + EV" scenario in 2030 has the highest CO2 emission reduction of 73% from electricity and vehicle usage, supplying 71% of the electricity demand of the city. The "PV + EV" system may reduce energy costs by 59% with estimated technology costs in 2030. Most of the energy generated from rooftop PV is consumed owing to large EV battery capacities, which can contribute to the rapid decarbonization of Bangkok City by 2050. [ABSTRACT FROM AUTHOR]

This research was conducted to study the flame instability of syngas derived from raw municipal solid waste (MSW) and its potential as a natural gas (NG) replacement in power generation. MSW syngas is a mixture of various components such as methane (CH4), nitrogen (N2), oxygen (O2), and hydrogen (H2), whereas NG is mainly composed of CH4 (>70%) and CO2 (>10%). The flame characteristics of these two gases are quite different thus a direct replacement of NG with MSW syngas is impossible. Improvements to MSW syngas combustion are needed through the augmentation of the gas with CH4 and H2 active additives at various ratios so that its flame characteristics are comparable to those of NG. A typical MSW syngas composed of 16.2% methane (CH4), 13.5% hydrogen (H2), 69.1% nitrogen (N2), and 0.6% oxygen (O2) (by vol.) is available in Thailand with great potential for use as an NG replacement. In this study, this gas is used as a representative fuel for improvement and is referred to as simulated Syngas 1. Its premixed flame was studied using a McKenna flat burner to understand its flame instability. Various percentages of CH4 and H2 were added to Syngas 1. Its flame characteristics were measured and compared to those of NG. These characteristics included the cellular flame, cell size, flat flame, flammability limit, and flame temperature. The results showed that the flame instability of Syngas 1 was significantly suppressed by adding minimal amounts of CH4 and H2. The new composition of Syngas 1 consisted of 19.3% methane (CH4), 19.0% hydrogen (H2), 61.2% nitrogen (N2), and 0.5% oxygen (O2) (by vol.). It yielded flame characteristics that were comparable to those of an NG flame. This study shows that MSW syngas can potentially replace NG in power generation. [ABSTRACT FROM AUTHOR]

Waste-to-energy (WtE) is national policy. From this view, WtE technology has been promoted. Many WtE projects in Thailand were unsuccessful due to several problems. This research aimed to analyze the key barriers impacting the WtE project development in Thailand. The Interpretive Structural Model (ISM) and Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) analysis tool have been used to evaluate the barriers that significantly in the development of WtE projects. In this study, WtE projects focused on electricity power generation in order to correspond to the Alternative Energy Development Plan (AEDP) target and power purchase agreement constrain of the government. The barriers were obtained from six sections consisting of social issues, environment, national policy, technology, economy, and project management. From six sections, there are 20 barriers that were identified. The ISM and MICMAC analysis showed that the key barriers impacting the WtE projects development were insufficient amount of waste and poor waste management planning. These two barriers correspond with many studies in Thailand and other countries. The project developers or investors must take these two barriers and other barriers with less impact mentioned in this study into account before developing the WtE projects in Thailand. [ABSTRACT FROM AUTHOR]

This study investigated and compared energy conservation levels between listed companies in energy-intensive industries and non-energy-intensive industries in Thai capital markets. It also tested the impact of energy conservation on firm performance using companies in the two industries. The sample for the study was sourced from 552 companies in the Stock Exchange of Thailand (SET) and 169 companies in the Market for Alternative Investment (MAI). The data was collected from the companies' annual reports spanning the period from 2016 to 2020. Descriptive analysis, independent sample t-test, and unbalanced panel data analysis were used to analyze data. The findings revealed that energy conservation scores for Thai-listed companies were generally stable, averaging between 0.45 and 0.46. It was also revealed that the energy conservation of companies in energy-intensive industries was significantly greater than that of companies in non-energy-intensive industries, with average scores of 0.55 and 0.43, respectively. Additionally, the study found that energy conservation has a positive impact on the firm performance of energy-intensive industries, while no significant impact in energy-intensive industries was recorded. The findings demonstrate that stakeholder and legitimacy theories can help explain how energy conservation benefits companies in terms of increased firm performance. [ABSTRACT FROM AUTHOR]

With rising electricity demand, heavy reliance on imports, and recent economic downturns due to the negative impact of the COVID-19 pandemic, supply chain bottlenecks, and the Russian invasion of Ukraine, Thailand is suffering severely from energy resilience risks. The government has therefore set a goal of decentralizing energy production through small-scale distributed renewable energy systems. To support their design and the planning process, we simulate multiple scenarios with wind turbines, photovoltaic systems, and battery storage for a model community in rural Nakhon Phanom, Thailand. Using the software NESSI4D, we evaluate and discuss their impact on energy resilience by considering environmental sustainability, economic attractiveness, and independence from the central power grid. To fill the gap of missing data on energy demand, we synthesize high-resolution load profiles from the Thailand Vietnam Socio-Economic Panel. We conclude that distributed photovoltaic systems with additional battery storage are only suitable to promote energy resilience if the government provides appropriate financial incentives. Considering temporal variations and local conditions, as well as a participatory decision-making process, are crucial for the long-term success of energy projects. Our advice to decision-makers is to design policies and regulatory support that are aligned with the preferences and needs of target communities. [ABSTRACT FROM AUTHOR]

Co-gasification of refuse derived fuel (RDF) and wood chips was experimented in the Nong Bua dual fluidised bed steam gasifier in Thailand. The effect of the mass of RDF pellets in the feed fuel (R/F ratio) was investigated on the performance of the entire process conditions and the product gas properties. The test results showed that the addition of small mass ratio of RDF pellets up to 30% did not affect the process operation conditions. The concentrations of H2, CO, CO2, and CH4 from a binary mixture of RDF pellets and wood chips were in the same ranges as that from pure wood chips. The lower heating value of the product gas was as high as 13.2–13.6 MJ/Nm3 for all the R/F ratio fuels. It is concluded that binary mixtures of RDF pellets and wood chips with the mass percent of R/F ratio of 10–30% are good feedstocks in the Nong Bua dual fluidised bed gasification process. The tar content, however, from the binary mixtures of RDF pellets and wood chips was higher than that of pure wood chips. The tar must be completely removed before the product gas of the RDF pellets can be utilised in the gas engine. [ABSTRACT FROM AUTHOR]

To reduce carbon dioxide emissions from traditional drying methods, this research investigated the use of microwave technology for drying Standard Thai Rubber (STR) in Thailand. Commercial microwave ovens were modified and integrated with the microwave emitting power control system to maintain the appropriate temperature levels to evaporate the moisture from rubber. Throughout the drying process, the temperature of the rubber was measured both internally and outside. The results revealed that STR5L and STR20 could be dried satisfactorily and met the requirements for standard Thai rubber properties by utilizing 500 W for 140 and 120 min, respectively. By keeping the temperatures less than 150 °C, rubbers' molecular structure is not destroyed from internal heat stress. Although utilizing less power for a longer period of time is possible, more energy was used, which is unfavorable. Compared to traditional hot air drying technologies, which take approximately 4–6 h for the drying process, microwave technology potentially reduces the drying time by half or more. If solar energy is used to supply electrical energy, 300,000 tons of Carbon dioxide can potentially be eliminated annually in the STR drying industry in Thailand by promoting approximately 1115 MW of Photovoltaic technology installations with the solar resources in southern Thailand. [ABSTRACT FROM AUTHOR]

Thailand is an agricultural country with several agro-industrial by-products that can be processed into fuels. Although producing ethanol from agro-industrial by-products is an interesting option, the process of distilling ethanol from fermented agricultural products requires a high temperature to increase the ethanol concentration from 10% to 95%. In this research, solar ethanol distillation equipment incorporating a solar parabolic collector with a vacuum heat absorber tube to increase efficiency by reducing heat loss was designed and developed. An electronic device was used to control the distillation process, maintain the required temperature, and make suitable adjustments to the solar radiation acceptance angles of the parabolic solar collector. Ethanol dilution at concentrations of 10%, 15%, and 20%, and Sato (Thai Rice Wine) were used as the reactant in the distillation process. The result of distilling ethanol distillation with a semi-automatic control using a vacuum-tube parabolic solar collector showed that the thermal efficiency of the receiver was 12.61%, 13.93%, 18.58%, and 17.40%, respectively. The thermal efficiency of the heat exchanger was 11.27%, 10.76%, 13.35%, and 12.35%, respectively. The final concentration of ethanol was 67%, 76%, 82%, and 80%, respectively, and the amount of the distilled ethanol was 330 mL, 352 mL, 398 mL, and 360 mL, respectively. [ABSTRACT FROM AUTHOR]

This research proposes an approach to estimate the number of different types of electric vehicles for a vast area or an entire country, which can be divided into a large number of small areas such as a subdistrict scale. The estimation methodology extensively utilizes the vehicle registration data in conjunction with Thailand's so-called EV30@30 campaign and GIS-based road infrastructure data. To facilitate the analysis, square grids are built to form cells representing the number of electric vehicles in any specific area of interest. This estimated number of electric vehicles is further analyzed to determine the energy consumption, calculate the recommended number of public chargers, and visualize an increase in the substation loads from those charging stations. The effectiveness of the proposed methods is demonstrated using the whole area of Thailand, consisting of five regions with a total area of 513,120 km2. The results show that the trucks contribute the most energy consumption while taxis rely on a lot of public chargers. The total energy consumption is about 79.4 GWh per day. A total of 12,565 public fast chargers are needed across the country to properly support daily travel, around half of them being located in the metropolitan area. [ABSTRACT FROM AUTHOR]

This article involves forecasting daily electricity consumption in Thailand. Electricity consumption data are provided by the Electricity Generating Authority of Thailand, the leading power utility state enterprise under the Ministry of Energy. Five forecasting techniques, including multiple linear regression, artificial neural network (ANN), support vector machine, hybrid models, and ensemble models, are implemented. The article proposes a hyperparameter tuning technique, called sequential grid search, which is based on the widely used grid search, for ANN and hybrid models. Auxiliary variables and indicator variables that can improve the models' forecasting performance are included. From the computational experiment, the hybrid model of a multiple regression model to forecast the expected daily consumption and ANNs from the sequential grid search to forecast the error term, along with additional indicator variables for some national holidays, provides the best mean absolution percentage error of 1.5664% on the test data set. [ABSTRACT FROM AUTHOR]

Energy expenditures are now the main cost for two businesses that generate huge incomes each year for Thailand, which are agribusiness and community tourism. As entrepreneurs have to share a portion of their income as energy utility bills each month. This is a factor which results in them getting a low net return. Recognizing the need for energy management for sustainable use in agriculture focusing on durian cultivation in Kantharalak district and community tourism in Sisaket province, this research used a newly developed optimization algorithm called Kouprey-inspired optimization (KIO) to assist energy management in smart agriculture to support community-based tourism. This was initiated with a smart energy node to reduce the energy and labor costs for volcanic durian planting and accommodation in community-based tourist attractions in Sisaket province. The results showed that the combination of the KIO algorithm and smart energy node allowed for efficient management of the volcanic durian orchards and the use of clean energy in combination with traditional electric power for volcanic durian cultivation and community-based tourism. As the research area in Sisaket province had eight hours of solar power per day, this was sufficient for smart agriculture and community-based tourism in the daytime and in the evening. Furthermore, this allowed operators in both the agricultural and tourism sectors to reduce the labor costs of the durian orchard business and community-based tourism by about 30%, and in the energy sector, the costs could be reduced by 50%. As a consequence, this prototype would lead to the expansion and trial in durian orchards in the Eastern Economic Corridor area, which is an important economic area producing durian for export of the country. [ABSTRACT FROM AUTHOR]

Solar energy is planned to undergo large-scale deployment along with Thailand's transformation to a carbon neutral society in 2050. In the course of energy transformation planning, the issue of energy infrastructure adaptation to climate change has often been left out. This study aims to identify climate-related risks and countermeasures taken in solar power plants in Thailand using thematic analysis with self-administered observations and structured interviews in order to propose points of consideration during long-term energy planning to ensure climate adaptation capacity. The analysis pointed out that floods and storms were perceived as major climate events affecting solar power plants in Thailand, followed by lightning and fires. Several countermeasures were taken, including hard countermeasures that require extensive investment. Following policy recommendations were derived from the climate-proofing investment scenario study. Policy support in terms of enabling regulations or financial incentives is needed for implementation of climate-proofing countermeasures. Public and private sectors need to secure sufficient budget for fast recovery after severe climate incidents. Measures must be taken to facilitate selection of climate-resilient sites by improving conditions of power purchase agreement or assisting winning bidders in enhancing climate adaptability of their sites. These issues should be considered during Thailand's long-term energy planning. [ABSTRACT FROM AUTHOR]

The objective of this study was to investigate the factors that influence the consumer adoption of renewable energy in Thailand. The study adopted an extended theory of planned behavior (TPB) by including three additional variables. The study applied a quantitative study methodology, with primary data collected using a survey of consumers in five major cities in Thailand. The data were analyzed using structural equation modeling (SEM). The findings of the study indicated that perception of self-effectiveness, environmental concern, renewable energy awareness, and beliefs about renewable energy benefits have a significant and positive effect on consumers' intention to adopt renewable energy. The cost of renewable was found to have a negative but non-significant influence on consumers' adoption of renewable energy, while risk/trust perception was found to have a positive but non-significant influence on consumers' adoption of renewable energy. The study concluded that stakeholders should take into account the aspects of perception of self-effectiveness, environmental concern, renewable energy awareness, and beliefs about renewable energy benefits when running campaigns to promote the consumer adoption of renewable energy in Thailand [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This empirical study assesses the effect of CO2 emissions, urbanization, energy consumption, and agriculture on Thailand's economic growth using a dataset between 1970 and 2018. The ARDL and the frequency domain causality (FDC) approaches were applied to assess these interconnections. The outcome of the bounds test suggested a long-term association among the variables of investigation. The ARDL outcomes reveal that urbanization, agriculture, energy consumption, and CO2 emissions positively trigger Thailand's economic growth. Additionally, the frequency domain causality test was used to detect a causal connection between the series. The main benefit of this technique is that it can detect a causal connection between series at different frequencies. To the understanding of the authors, this is the first study in the case of Thailand that will apply the FDC approach to capture the causal linkage between GDP and the regressors. The outcomes of the causality test suggested that CO2 emissions, urbanization, energy consumption, and agriculture can predict Thailand's economic growth in the long term. These outcomes have far-reaching implications for economic performance and Thailand's macroeconomic indicators. [ABSTRACT FROM AUTHOR]