Author Panel

Results for: renewable energy

10 results found in 0.6 seconds
Search Sources
Database
Complete
10 results
PubMed/NCBI
Loading...
0 results
arXiv
Loading...
0 results
Semantic Scholar
Loading...
0 results
CrossRef
Loading...
0 results
2026 barre2026factors DATABASE
Factors influencing Somalia household's willingness to pay renewable energy: employing structural equation modeling.

Barre, Galad Mohamed; Mohamud, Ahmed Hassan

Scientific reports

This study used PLS path analysis and structural equation modelling (SEM) to examine the factors influencing Somali households' willingness to pay for renewable energy. Non-probability purposive sampling was used in a quantitative survey to select respondents from Mogadishu, the capital city of Somalia. 300 home power bill payers who were informed about energy costs and renewable energy requirements were given a standardized closed-ended questionnaire. Following data cleaning, SPSS version-25 and SmartPLS-4 were used to analyze 255 valid replies using descriptive and inferential statistics. With an R2 of 0.428, the structural model has moderate explanatory power, accounting for 42.8% of the variance in willingness to pay for renewable energy. The model's robustness is confirmed by an adjusted R2 of 0.402. The findings showed that consumer intention, environmental concern, perceived behavioral control, subjective norms, and Attitude have a positive significant impact on willingness to pay for renewable energy. Belief about the cost of renewable energy shows no significant relationship with willingness to pay for renewable energy. The results of the moderation analysis indicate that the relationships between environmental concern, subjective norms, and attitude with willingness to pay for renewable energy are considerably moderated by customer intention. However, the relationship between perceived behavioral control and belief about the cost of renewable energy with willingness to pay for renewable energy is not moderated by consumer intention. The findings offer policymakers and renewable energy stakeholders insights to increase adoption rates.
2026 azeem2026deep DATABASE
Deep learning & PSO-based optimisation of data centre energy costs using renewable sources.

Azeem, Fawad; Nazir, Husnain; Arshad, Jehangir; Joyo, M Kamran; Ahmad, Izanoordina; Kadir, Kushsairy; Chauhdary, Sohaib Tahir; Noaman, Noaman M

Scientific reports

In this research work, an energy management framework for a data centre powered by a combination of solar and grid energy is proposed. A service-level objective (SLO) is assigned to every individual zone of the data centre, which is specified by each data centre's energy requirement. Jobs with the same SLO will be assigned to the same data centre zone, and each zone will be fuelled by renewable source with a chance of generating electricity equal to or greater than the area's need. To address the intermittency issues associated with renewable energy sources and reduce SLO violations, an effective mapping strategy for renewable energy sources and data centre zones was developed using the reinforcement learning technique Deep Q-Network (DQN) algorithm to ensure maximum data centre uptime. A Particle Swarm Optimisation (PSO) algorithm is then applied to maximise the use of renewable power in meeting the data centre demand. This research aids in reducing the energy utilisation impact of data centres on the national grid, thereby avoiding a national energy deficit. Consequently, the anticipated use of renewable energy will minimise environmental degradation while boosting the country's economy. Data centres are another potential energy buyer from geo-distributed renewable energy sources in Pakistan's power market, according to our analysis.
2026 khaled2026dynamic DATABASE
Dynamic multi-period optimal power flow considering renewable energy degradation and temperature derating.

Khaled, Bassem; Abdelaziz, Almoataz Y; Attia, Mahmoud A; Khamees, Amr K

Scientific reports , 16

Renewable energy sources, such as solar and wind, play vital role in reducing emissions of carbon dioxide and combating climate change. However, their stochastic behavior has an impact on modern electric networks. Hence, Modelling variability and uncertainty in optimal power flow (OPF) concerns is critical for ensuring dependable and environmentally friendly grid operations. This study investigates the impact of climate change on the integration of wind and solar energy into power systems, with a specific focus on how temperature variations affect the performance and optimal dispatch of wind turbines and solar photovoltaic (PV) systems. To minimize total costs and carbon emissions, both single-objective and multi-objective optimization problems are developed, incorporating temperature-dependent de-rating effects on PV modules and wind turbines. Mayfly algorithm (MA) is applied to the IEEE-30 bus system for Optimal Power Flow (OPF), achieving a 0.6% and 0.5% reduction in fuel cost and carbon emissions compared to PSO and further techniques in single-objective OPF. The study extends to Stochastic OPF (SOPF) on a modified IEEE-30 system with two wind farms and one PV plant. Where, temperature-dependent models are used for both wind energy and solar energy. To additionally encourage and increase the reliance on renewable energy, Carbon credit concept is added to the total cost objective function as novel contribution. The results show that when the carbon credit is taken into account the total cost is reduced by 0.8% compared to the case when it is not considered. To assess the climate change impact, it is found that at 40 °C, the total cost and emissions increase by 21% and 45.67% respectively when minimizing cost, and by 9.67% and 45.7% when minimizing emissions. The multi-period analysis evaluates the evolution of renewable penetration over the 25-year lifetime by considering the gradual reduction in renewable output caused by annual degradation and temperature-related derating. The renewable penetration limits are therefore updated across the planning period to reflect the reduced contribution of PV and wind generation over time. where, this impact is on total cost and carbon emissions over 25-year lifetime through both single and multi-objective dynamic Multi-Period SOPF (MPOPF) problem. For single-objective MPSOPF problem, after 25 years, the results at 40 °C show that the total cost and emissions increase by 24.96% and 51.8% respectively when minimizing the total cost compared to the results obtained at the beginning of wind and solar plants operation. Moreover, Multi-Objective SOPF is solved, a fuzzy-based Pareto front is used, the outcomes show that when the ambient temperature rises to 40 °C, the compromise solution shows an increase of 16.65% total cost and 41.87% carbon emission respectively. For Multi-Objective MPSOPF problem, at 40 °C, the compromise solution shows a 20.16% cost and 60.1% emission increase after 25 years compared to the case when the temperature rise and degradation effect are not taken into account. The findings reveal that climate change and degradation adversely affect renewable energy integration, resulting in increased reliance on thermal power and emphasizing the need for informed planning of sustainable energy infrastructures and re-powering the renewable energy resources at the end of their lifetime.
2026 foroughian2026optimal DATABASE
Optimal operation of multi-carrier energy systems integrated with renewable energy sources and hydrogen storage systems.

Foroughian, Saina; Bijan, Zohreh Aghaie Joki; Karimi, Hamid; Hasanzadeh, Saeed

Scientific reports

Multi-energy systems are one of the main solutions to facilitate the integration of renewable energy resources in the smart energy system. To this end, this paper presents a comprehensive structure for the energy system that integrates the electrical, hydrogen, and water sections for sustainable management of modern energy systems. The presented model offers cooperative scheduling for neighbor multi-energy systems that provides the opportunity of local energy trading among them. Also, it focuses on the water system and seeks to supply potable water for the energy systems by a water well, desalination unit, and water storage tank. Besides, compressed air energy storage is developed to utilize the surplus generation of renewable energy to provide an efficient operation for the system. To control the uncertain nature of renewable generation, the energy systems can take part in the electrical and thermal demand-side programs to manage their consumption in response to the signal prices. The proposed model is tested on a standard case study, and the numerical results show that the cooperation among energy systems reduces their operating cost and unserved energy by $ 23.91 and 64.317 kWh compared to autonomous operation.
2025 national renewable energy laboratory (nrel)2025renewable DATABASE
Renewable Energy Technical Potential and Supply Curves for the Contiguous United States: 2024 Edition

National Renewable Energy Laboratory (NREL), Golden, CO (United States); Lopez, Anthony; USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office; USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Wind Energy Technologies Office; USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Geothermal Technologies Office; Zuckerman, Gabriel; Pinchuk, Pavlo; Gleason, Michael; Rivers, Marie; Roberts, Owen; Williams, Travis; Heimiller, Donna; Thomson, Sophie-Min; Mai, Trieu; Cole, Wesley

Renewable Energy

2024 m.saleh2024the DATABASE
The challenges of sustainable energy transition: A focus on renewable energy

Hosam M.Saleh; Amal I.Hassan

Applied Chemistry for Engineering

Energy is both a fundamental necessity and a driving force behind human activities. Throughout history, energy consumption has steadily risen, evolving from basic needs like food and fire for early humans to complex industrial and technological requirements today. Transitioning to a sustainable energy system requires a policy framework that empowers developing nations to promote green industries, diversify their sectors, and accelerate growth while addressing climate change and related challenges. In response to the urgent need for a global transition towards sustainable energy sources, this research explores the pivotal roles of technology, research, and policy in advancing renewable energy solutions. Motivated by the growing environmental challenges associated with conventional energy sources, the primary goal of this study is to shed light on the multifaceted strategies that facilitate the widespread adoption of renewable energy and contribute to mitigating climate change. Through an extensive analysis of renewable energy technologies, research contributions, and policy frameworks, this research uncovers critical insights. Our findings reveal how technological innovations have revolutionized renewable energy sources, making them more efficient, affordable, and scalable. Furthermore, research efforts have identified new opportunities and addressed technical challenges, while also assessing the environmental and societal impacts of renewable energy adoption. Crucially, this study underscores the indispensable role of policy in driving renewable energy transitions. Governments worldwide play a pivotal role in incentivizing renewable energy development through financial incentives, regulatory mandates, and research and development support. Moreover, these policies aim to promote energy efficiency, conservation, and equitable access to sustainable solutions. The results of this research emphasize that the transition to renewable energy is not only a viable solution to climate change but also an opportunity to create green jobs, enhance energy security, and reduce greenhouse gas emissions. The potential for a sustainable future powered by renewable energy is within reach, and this study serves as a guidepost for realizing this transformative vision.
2024 usdoe office of energy efficiency and renewable energy (eere)2024mcgrath DATABASE
McGrath, Alaska Community Energy Plan [Slides]

USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Building Technologies Office; Cooke, Aaron; USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Geothermal Technologies Office; USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office; USDOE Office of Energy Efficiency and Renewable Energy (EERE), Office of Sustainable Transportation. Vehicle Technologies Office (VTO); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Water Power Technologies Office; USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Wind Energy Technologies Office; National Laboratory of the Rockies (NLR), Golden, CO (United States); Martinez Biro, David; Levey, Milena; Corrigan, Anne

Renewable Energy

2024 zhao2024how DATABASE
How does artificial intelligence promote renewable energy development? The role of climate finance

Congyu Zhao; Kangyin Dong; Kung-Jeng Wang; Rabindra Nepal

Energy Economics

Scholars, stakeholders, and the government have given significant attention to the development of renewable energy in recent times. However, previous research has failed to acknowledge the potential impact of artificial intelligence on advancing renewable energy development. Drawing insights from a global dataset encompassing 63 countries over the period 2000 – 2019, this paper provides significant observations regarding the influence of artificial intelligence on the progress of renewable energy, by using the Instrumental Variable Generalized Method of Moments model. We also explore their asymmetric nexus, and the potential mediation effect. Moreover, this study explores the moderating role of climate finance and highlights the following interesting findings. First, artificial intelligence contributes significantly to the enhanced development of renewable energy, and this primary finding holds after two robustness tests of changing independent and dependent variables. Second, artificial intelligence has an asymmetric effect on renewable energy development, and their nexus is closer in countries with lower levels of renewable energy development. Thid, artificial intelligence works on renewable energy development through technology effect and innovation effect. Fourth, climate finance also presents direct benefits to renewable energy development; simultaneously, climate finance plays an effective moderating role in the relationship between artificial intelligence and renewable energy development. These findings inspire us to propose policy implications to promote the enhanced development of renewable energy.
2022 holechek2022a DATABASE
A Global Assessment: Can Renewable Energy Replace Fossil Fuels by 2050?

J. Holechek; H. Geli; M. Sawalhah; Raul Valdez

Sustainability

Our study evaluated the effectiveness of using eight pathways in combination for a complete to transition from fossil fuels to renewable energy by 2050. These pathways included renewable energy development; improving energy efficiency; increasing energy conservation; carbon taxes; more equitable balancing of human wellbeing and per capita energy use; cap and trade systems; carbon capture, utilization, and storage; and nuclear power development. We used the annual ‘British Petroleum statistical review of world energy 2021’ report as our primary database. Globally, fossil fuels, renewable (primarily hydro, wind and solar), nuclear energy accounted for 83%, 12.6%, and 6.3% of the total energy consumption in 2020. To achieve zero fossil fuel use by 2050, we found that renewable energy production will need to be increased by up to 6-fold or 8-fold if energy demand is held constant at, or increased 50% from, the 2020 energy demand level. Constraining 2050 world energy demand to a 25% increase over the 2020 level, improves the probability of achieving independence from fossil fuels. Improvements in energy efficiency need to accelerate beyond the current rate of ~1.5% per year. Aggressive application of energy conservation policies involving land use and taxation could potentially reduce world energy use by 10% or more by 2050. Our meta-analysis shows that the minimum level of per capita energy consumption that would allow 8 billion people to have a ‘Decent Living Standard’ is on average ~70 GJ per capita per year, which is 93% of the 2020 global average. Developed countries in temperate climates with high vehicle-dependency needed ~120 GJ per capita year−1, whereas equatorial countries with low vehicle-dependency needed 30 GJ per capita year−1. Our meta-analyses indicated replacement of fossil fuels with renewable energy by 2050 may be possible but will require aggressive application of all eight pathways, major lifestyle changes in developed countries, and close cooperation among all countries.