This paper presents a novel hybrid model that integrates predictive and optimization techniques to enhance the scheduling and management of electricity generation in large-scale power systems, with a focus on the variability of photovoltaic (PV) energy. By combining a long short-term memory (LSTM) neural network with an optimization framework, the model forecasts PV power generation over a one-month horizon using historical data, validated against actual production. The optimization component, built on a refined large-scale power system model, incorporates these predictions using a block representation approach to simulate diverse generation technologies, including natural gas, fossil fuel-based thermal units, hydroelectric, PV, nuclear, and wind power plants. This integrated approach addresses the stochastic nature of renewable sources, distinguishing it from prior studies that focus solely on prediction or optimization. The Argentine Interconnection System (SADI) serves as the case study, leveraging over a decade of time-series data to evaluate the model’s performance. Results demonstrate reliable prediction and scheduling capabilities, achieving a low prediction error of approximately 0.01% for key PV sources. Implemented in Python within the Spyder environment, with TensorFlow and Keras for LSTM predictions and PYOMO for optimization, the model offers a practical and effective solution for system operators to optimize resource allocation in renewable-heavy power systems.

It is crucial to identify alternative energy sources owing to the ever-increasing demand for energy and the other environmental problems associated with using fossil fuels. Biomass as a source of bioenergy is considered a promising alternative to fossil fuels. This study aims to optimize the biomass supply chain by developing an integrated model incorporating typical tactical supply chain decisions based on market or demand selection decisions. To this end, a novel mixed-integer linear programming (MILP) model is proposed to maximize the profit of the corresponding biomass supply chain and to commercialize electricity production by selecting electricity demand and making supply chain decisions regarding power plant operations, biomass feedstock purchase and storage, and biomass transport trucks. Owing to the intricacy of the MILP model, a fix-and-optimize-based solution strategy is developed and validated by applying it to several instances of a real-world case study. The results demonstrate that the proposed strategy can significantly reduce computational time while preserving high solution quality. Additionally, it helps improve planning and decision-making as it reveals the effect of essential biomass logistics characteristics on routing outcomes.

Renewable energy development is important for improving energy security and economic growth in Indonesia. This study identifies the best renewable energy potential in Southwest Sumba, East Nusa Tenggara Province, using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method based on 5C criteria: Consolidated, Controllable, Continue, Clean, and Cheap. The research uses a multi-criteria decision-making approach, using primary data from expert interviews and secondary data from literature reviews. The TOPSIS analysis shows that solar energy has the highest preference value, followed by bioenergy and hydropower. Technical assessments show important implementation requirements for each renewable energy option. The study recommends prioritizing solar energy development, supporting bioenergy projects, improving micro-hydro facilities, and creating clear renewable energy policies. Success depends on cooperation between stakeholders and aligning renewable energy development with regional sustainability and community needs. These efforts can help Southwest Sumba develop its renewable energy sector and contribute to national energy security goals.

In his research, he has established an analysis of the consumption of renewable energy and its impact on CO2 emissions in Peru, 1990-2020. The research employs a quantitative approach and longitudinal non-experimental design, with a multiple linear regression model. It uses time series drawn from the World Bank on renewable energy consumption and energy consumption. A progressive increase was reflected mainly driven by industrial growth, fossil fuel consumption and changes in consumption and production patterns.

There is a global trend towards adopting green energy, with solar energy being the primary source derived from solar panel technologies. Solar panels can generate enough power for general and household use. However, to effectively function and serve their purpose, they require regular cleaning and effective maintenance, and robotic cleaning is among the current applicable technologies. This research aims to determine the intention of using solar panel cleaning robots in Thailand for individual solar panel users. The study was hinged on the extended C-TAM-TPB model. The quantitative survey study design was employed using primary data collected from individual solar panel users in their households. 419 respondents were used to collect the data. The C-TAM-TPB model proposed using reliability, validity, and model fitness which employed confirmatory factor analysis (CFA). They adopted structural equation modeling (SEM) in the evaluation of the variables' relationships and study hypotheses. Subjective norms and trust in technology, individual control perception, and awareness of renewable energy significantly and positively affected behavioral intention to use solar panel cleaning robots as indicated by the study. Trust in technology, awareness of renewable energy, and environmental concerns were found to be pivotal mediators to the attitude effect on individual users' intention to act in using solar panel cleaning robots. The authors recommend that to improve the adoption of solar panel cleaning robots; the concerned stakeholders should consider, firstly, enhancing trust in the technology of these robots, which is crucial, focusing on aspects like reliability, privacy, security, and reputation. Secondly, considering the influence of subjective norms, including perceptions from family, friends, colleagues, and experts, is essential. Perceived behavioral control should also be a focal point, encompassing self-efficacy, resources, and complexity. Moreover, increasing awareness of renewable energy and environmental benefits is vital to encourage individual adoption. The research also recommended that to encourage the adoption and use of solar panel cleaning robots, the aspects that should be emphasized include subjective norm, perceived behavior control, trust in technology, and awareness of renewable energy.

The study aimed to demonstrate the impact of Green Supply Chain Management (GSCM) with its dimensions (Green IT, Green Manufacturing and Packaging, Green Storing, Green Purchasing, Green Marketing) on the quality of services in renewable energy companies in Jordan. In addition, the study also aimed to measure the impact of intellectual capital on the impact of GSCM on the quality of services in renewable energy companies in Jordan. By adopting the survey/sampling method, data was collected from the study population of (482) companies, and the study sample consisted of (260) managers of renewable energy companies in Jordan using a questionnaire. The study reached several results, the most important of which are: the existence of an impact of green supply chain management on the quality of services in renewable energy companies, that intellectual capital has modified and enhanced the effect, and that renewable energy companies in Jordan seek to use environmentally friendly inputs in their production at a very high rate, several recommendations emerged from these results, most notably: that companies constantly review the production processes followed by suppliers to ensure their compliance with environmental specifications.

The increase in economic activity in the industrial sector and the rapid growth of the world population have stimulated an increase in energy demand. In 2004, Indonesia earned the status of a net importer of oil so that it becomes a challenge for the Indonesian government in developing the use of renewable energy to achieve ideal conditions for national energy security. Indonesia has the potential for large amounts of renewable energy sources, one of which is palm-based biodiesel. The mandatory biodiesel policy program was implemented in 2008 with a biodiesel content of 2.5% and gradually until 2019 with a biodiesel content of 30% (B30). The mandatory biodiesel policy is closely related to the achievement of the Sustainable Development Goals (SDGs), and the concept of maintaining the balance of Trilemma Energi. The current energy management and utilization policies in Indonesia continue to increase in line with modern life consumption patterns that require a more environmentally friendly energy variable for energy absorption in Indonesia, especially renewable energy. The purpose of this research is to determine the integration model of palm-based biodiesel policy as a renewable energy commodity to support energy security. This study uses several strategic frameworks by combining a quantitative approach through the perspective of the Balanced Scorecard (BSC) and measuring the technology coefficient using the Technology Contribution Coefficient (TCC), as well as a qualitative approach with the Business Model Canvas (BMC) and the design of the Omnibus Law. Data were collected through Focus Group Discussion (FGD) and Expert Opinion (EO) which were validated by Structural Equation Modeling-Partial Least Square (SEM-PLS) using a sample of 40 respondents from related agencies. The results showed that based on the SEM-PLS validation of 20 BSC perspective variables, two invalid variables were obtained, namely the variable efficiency port service cost and value-added creation which had a P value> 0.05. Meanwhile, Indonesia's TCC score is quite high, namely 0.787, which means that Indonesia is quite aggressive in developing biodiesel and its policies. Based on the results of the FGD expert, it was obtained that the BMC initiates the helicopters to view current biodiesel developments. And 10 regulations have been drafted into a proposed draft Omnibus Law through an action plan.

Solar power is a mature and fast-growing field based on single crystal silicon wafer technology. Although China, Europe and the US are the main markets, 80 percent of the modules are manufactured in Asia. In Vietnam, modules are manufactured in collaboration with Chinese and American manufacturers. In 2017, there were 5 GW of solar panels produced in Vietnam, accounting for 7% of the global market. The domestic solar market is expected to peak at around 1.8 GW/year according to the targets set out in the revised PDP 7. The domestic module production capacity, currently devoted entirely to export, is around 5.2 GW/year, three times the expected maximum capacity of the domestic market. In that context, due to the normal size of factories, only a few parts factories can sell to the domestic market, while the majority still must rely on exports. But it is important to map out a clear roadmap for 12 GW of solar power that will encourage the formation of EPC companies and other domestic service companies in Vietnam to build factories according to the plan. Construction, operation, maintenance, and production for the domestic market has the potential to increase Vietnam's GDP by about 0.25% by 2030 and create more than 25,000 jobs.

Based on the financial statements of real estate companies listed on Vietnamese stock market, the study has been conducted on factors affecting capital structure. The paper uses GLS (generalized least squared) estimation method related to panel data as well as testing to select the most appropriate model. Research results show that profitable real estate businesses, the ratio of fixed assets to total assets and the number of years of operation have a negative effect on capital structure. In contrast, renewable energy, size and growth are three factors that have positive effects on capital structure. In addition, the corporate income tax rate does not affect the capital structure decisions of real estate businesses. Through research, recommendations for the real estate business executives have been proposed to build an effective capital structure.

The aim of this study is focused on how monetary, energy consumption and other factors affect economic growth of the country of Vietnam. Based on collected secondary data covering from the World Bank and Vietnam’s General Statistics Office from 1985 to 2019, and some data collected from the State Bank of Vietnam, Vector Autoregressive Model was considered to apply in order to investigate this relationship. Results show that there exists an association among monetary policy, renewable energy and the country’s economic growth. Especially, the country’s exchange rate shows no influence on its economic growth while interest rate has negative effects and particularly money supply and renewable energy have a positive influence on the same direction and has a strong impact on economic growth.