Impacts of Solar Electricity Generation on the Thai Electricity Industry

Wattana, Buncha; Aungyut, P.

doi:10.14710/ijred.2022.41059

Cited by 19 publications

(14 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This amount of energy that is both ubiquitous and unlimited is more than sufficient to take care of the power requirements of the whole globe [138], [139]. The pursuit of a kind of energy that is both environmentally friendly and sustainable has quickly become a serious worldwide concern, especially in the world that we live in today [140]. The fast collapse of conventional energy sources such as oil and fossil fuels, together with worldwide environmental issues such as global warming and the increased need for electricity and energy, are the primary factors that have led to the emergence of this crisis [141].…”

Section: A Ai and ML Techniques In Renewable Energy Forecasting 1) So...mentioning

confidence: 99%

Harnessing a Better Future: Exploring AI and ML Applications in Renewable Energy

Nguyen,

Paramasivam,

Dong

et al. 2024

JOIV : Int. J. Inform. Visualization

View full text Add to dashboard Cite

Integrating machine learning (ML) and artificial intelligence (AI) with renewable energy sources, including biomass, biofuels, engines, and solar power, can revolutionize the energy industry. Biomass and biofuels have benefited significantly from implementing AI and ML algorithms that optimize feedstock, enhance resource management, and facilitate biofuel production. By applying insight derived from data analysis, stakeholders can improve the entire biofuel supply chain - including biomass conversion, fuel synthesis, agricultural growth, and harvesting - to mitigate environmental impacts and accelerate the transition to a low-carbon economy. Furthermore, implementing AI and ML in combustion systems and engines has yielded substantial improvements in fuel efficiency, emissions reduction, and overall performance. Enhancing engine design and control techniques with ML algorithms produces cleaner, more efficient engines with minimal environmental impact. This contributes to the sustainability of power generation and transportation. ML algorithms are employed in solar energy to analyze vast quantities of solar data to improve photovoltaic systems' design, operation, and maintenance. The ultimate goal is to increase energy output and system efficiency. Collaboration among academia, industry, and policymakers is imperative to expedite the transition to a sustainable energy future and harness the potential of AI and ML in renewable energy. By implementing these technologies, it is possible to establish a more sustainable energy ecosystem, which would benefit future generations.

show abstract

Section: A Ai and ML Techniques In Renewable Energy Forecasting 1) So...mentioning

confidence: 99%

Harnessing a Better Future: Exploring AI and ML Applications in Renewable Energy

Nguyen,

Paramasivam,

Dong

et al. 2024

JOIV : Int. J. Inform. Visualization

View full text Add to dashboard Cite

show abstract

“…In addition, the absence of a smart grid that integrates conventional and renewable energy sources and the restricted role of research and development in energy storage technologies are regarded as technological obstacles (Sambodo et al, 2022). This prospective smart grid technology will promote access to healthier, cleaner, and more long-lasting resources (Elrahmani et al, 2021), whereas the development of an energy storage system appears to be one option for addressing the intermittent nature of solar energy (Wattana & Aungyut, 2022).…”

Section: Policy Implicationsmentioning

confidence: 99%

Sustainable Long-Term Energy Supply and Demand: The Gradual Transition to a New and Renewable Energy System in Indonesia by 2050

Yudiartono

Windarta

Adiarso

2023

Int. J. Renew. Energy Dev.

View full text Add to dashboard Cite

The objective of this work is to evaluate long-term energy demand and supply decarbonization in Indonesia. On the demand side, electric vehicles and biofuels for transportation and induction stoves and urban gas networks for households were considered. Based on the National Energy Policy, primary energy supply projections optimized NRE power plant use and increase NRE's position in the national energy mix. A Low Emissions Analysis Platform (LEAP) model evaluates 2020–2050 energy demand predictions and low-carbon energy systems. This study's sustainable transition options require two basic technical advances. First, electric vehicles and induction stoves would reduce oil fuel usage by 228.34 million BOE and LPG consumption by 24.65 million BOE. Second, power generation should be decarbonized using NRE sources such as solar, hydro, biomass, geothermal, and nuclear. In 2050, solar power (40 GW), hydropower (38.47 GW), geothermal power (10 GW), and other NRE (24.45 GW, 18.67 GW of which would be biomass power) would dominate NRE electrical capacity. Biomass co-firing for coal power plants would reach 36.35 million tons in 2050. In 2035, the Java-Bali or West Kalimantan system will deploy 1 GW of nuclear power reactors, rising to 4 GW by 2050. Under the Transition Energy (TE) scenario, by 2025 and 2050, new and renewable energy would make up 23% and 31% of the primary energy mix, respectively, reducing GHG emissions per capita. According to predictions, annual GHG emissions per capita will decline from the BAU scenario's 4.48 tonne CO2eq/capita in 2050 to the TE scenario's 4.1 tonne.

show abstract

“…The impact analysis of wind farms in the Jeju island power system by using the PSCAD/EMTDC program was shown in [21]. Wattana and Aungyut [22] analysed the impacts of solar electricity generation on the Thai electricity industry. An analysis of the impact of the integration of large photovoltaics generation capacity and optimization of photovoltaics capacity in Taiwan using the PSS/E software was conducted in [23].…”

Section: Introductionmentioning

confidence: 99%

Impact of large-scale renewable power plants on the tie-line loadings of a Vietnamese power network

Bao

Ninh

Lâm³

et al. 2023

IJPEDS

View full text Add to dashboard Cite

<p>The rapid integration of wind power and power solar plants into the electrical power system has caused several operation issues in recent decades. To investigate the impact of large-scale renewable power plants on the tie-line loadings of a practical power grid, the 110 kV power network of Binh Dinh province, Vietnam is utilized in this paper. This power network with and without three solar power plants (including Fujiwara plant with a peak capacity of 50 MWp, Cat Hiep plant with a peak capacity of <br />49.5 MWp, and Dam Tra O plant with a peak capacity of 50 MWp) and three wind power plants (including Nhon Hoi plant with a capacity of 60MW, Phuong Mai 1 plant with a capacity of 26.4 MW, and Phuong Mai <br />3 plant with a capacity of 20.7 MW) is modeled by using the PSS/E software to examine the tie-line loadings capacity. The simulation results showed that the tie-line loadings of the current power network increase when six renewable power plants are integrated into this network. Therefore, three different scenarios to upgrade the existing power network are proposed. The final results demonstrated the efficiency of the proposed scenarios in solving the tie-line loadings issue of the practical power network.</p>

show abstract

Impacts of Solar Electricity Generation on the Thai Electricity Industry

Cited by 19 publications

References 37 publications

Harnessing a Better Future: Exploring AI and ML Applications in Renewable Energy

Harnessing a Better Future: Exploring AI and ML Applications in Renewable Energy

Sustainable Long-Term Energy Supply and Demand: The Gradual Transition to a New and Renewable Energy System in Indonesia by 2050

Impact of large-scale renewable power plants on the tie-line loadings of a Vietnamese power network

Contact Info

Product

Resources

About