Future Renewable Energy Research Articles

The Clean Development Mechanism and Corporate Financial Performance: Empirical Evidence from India

An important factor supporting the social economy's sustainable growth is the industry's development in renewable energy. India has a sophisticated system that combines finance, resources, technology, and management to grow, expand, and modernize the renewable energy industry. Renewable energy finance is a new area of public policy that will significantly affect international investors and calls for innovation and study. Many academics held the opinion that issues with renewable energy were limited to science and engineering until recently. But rather than relying on science and technology, the future of renewable energy increasingly lies on financial accessibility. The rapidly growing renewable energy business in India presents a great opportunity for financial gain. The current paper's goal is to assess the financial performance of India's renewable energy sector using a variety of financial metrics and various ratios. The different industrial stakeholders are impacted by the financial performance of every industry. Data from the corresponding firms' annual reports was used to analyze the financial performance of renewable energy companies, and the study's findings were determined using a t-test. According to the study's findings, India's renewable energy companies are still in their infancy and are working hard to keep their steady financial positions. Keywords: Financial Performance, Renewable Energy, Financial Ratio, India, Global Environment.

Analysis of solar energy potentials of five selected south-east cities in nigeria using deep learning algorithms

This study presents a meticulous examination of the solar energy potential of five selected metropolitan cities (Abakaliki, Awka, Enugu, Owerri, and Umuahia) in Eastern part of Nigeria using deep learning algorithm, specifically the Long Short-Term Memory (LSTM) model. These cities, despite being characterized by extended rainy seasons and a high level of cloudiness, are suitable environment for solar power generation and investment opportunities. The employed methodology capitalized on the LSTM deep learning approach to analyze and predict energy generation, utilizing comprehensive hourly weather data from the National Airspace Agency (NASA). The data set comprised various parameters, such as date/time, solar azimuth angle, temperature, humidity, wind speed, wind direction, cloud cover, and power, enabling a thorough analysis of each city. To ensure accuracy, energy prediction capabilities were benchmarked against real-time datasets from a solar power plant in Ulsan, South Korea, thereby training and fine-tuning the model for precision. The LSTM model's performance metrics were maintained at a learning rate of 0.07, a batch size of 150, and a train-test split ratio of 0.8 to 0.2. Data validation exhibited a mean square error (MSE) of 0.01, demonstrating the model’s reliability. Results showed Enugu as having the highest solar energy potential, averaging 6.25 kWh/day, while Awka registered the most substantial electricity demand across various sectors. These findings highlight the substantial potential for photovoltaic (PV) power systems and advocate for the immediate implementation of renewable energy policy in the selected cities. These are expected to bring about significant implications for future renewable energy environmentally friendly investments in Nigeria and globally.

Metal–organic frameworks for next-generation energy storage devices; a systematic review

The future of renewable energy and sustainable transportation depends on advanced energy storage technologies.

Envisioning the Future Renewable and Resilient Energy Grids—A Power Grid Revolution Enabled by Renewables, Energy Storage, and Energy Electronics

Envisioning the Future Renewable and Resilient Energy Grids—A Power Grid Revolution Enabled by Renewables, Energy Storage, and Energy Electronics

Contribution of ChatGPT and Other Generative Artificial Intelligence (AI) in Renewable and Sustainable Energy

In the dynamic field of renewable and sustainable energy, Artificial Intelligence (AI) integration has emerged as a crucial catalyst for enhancing efficiency, cost reduction, and addressing multifaceted challenges. This work delves into the performance and contributions of ChatGPT, a prominent representative of large AI language models, within the context of renewable and sustainable energy. The research endeavors to explore ChatGPT's efficacy and role across a diverse spectrum of renewable energy aspects, spanning solar energy, wind energy, biomass energy, hydropower, geothermal energy, energy storage, grid integration, energy efficiency, materials science, policy, economics, environmental impact, energy accessibility, and advanced biofuels. The investigation commences with solar energy, where ChatGPT showcases impressive capabilities in data analysis, modeling, and forecasting. It facilitates solar panel placement optimization, energy production prediction, and overall efficiency enhancement. Likewise, in the realm of wind energy, ChatGPT proves invaluable for tasks such as wind turbine control, maintenance scheduling, and wind speed prediction. In biomass energy, ChatGPT aids in feedstock selection, process optimization, and emissions reduction. In the hydropower sector, it supports reservoir management and river flow prediction, thereby optimizing energy output. Geothermal energy benefits from ChatGPT's geological analysis, reservoir modeling, and exploration strategies. Energy storage solutions are indispensable for grid stability, and ChatGPT contributes to the advancement of cutting-edge battery technologies, augmenting energy storage capacity and lifespan. Furthermore, ChatGPT's insights foster the evolution of smart grids, ensuring efficient energy distribution and effective demand management. Additionally, ChatGPT provides invaluable insights into policy and economics, offering recommendations for incentivizing the adoption of renewable energy sources while considering environmental impact and sustainability. It also contributes to the advancement of tidal and wave energy technologies, as well as the development of advanced biofuels. The outcomes of this research paper underscore the potential of AI and ChatGPT in shaping the future of renewable energy, expediting progress toward a sustainable energy ecosystem.

Modeling and simulation of Khat waste fast pyrolysis for energy recovery

Bioenergy is now recognized to be capable of providing the vast majority of predicted future renewable energy supply. Biomass is currently considered a common and commonly used renewable energy source. This study depends upon the investigation of khat waste using Aspen Plus software, which is required for creating environmentally friendly energy sources capable of improving our access to energy and economic sustainability. The outcome of the study is to understand the characteristics of the pyrolysis process without conducting a time-consuming, expensive, and complex procedure. The results of the investigation will be useful in determining the best feedstock for the formation of biofuel. Aspen Plus software simulates several ash-free organic components, including carbon, oxygen, nitrogen, hydrogen, and sulfur, with results like 45.72 % for carbon, 5.84 % for hydrogen, 0.43 % for nitrogen, and 38.56 % for oxygen. The production of biofuel is affected by processing parameters such as temperature and total mass flow rate. During reactions with the same mass but different temperatures, the bio-oil declined from 600 °C to 800 °C, while the maximum gas emission climbed quickly and the biochar reduced. In addition, it was recovered from Khat waste and proved to have an energy efficiency of 80.75 % and a net energy capacity of 134.25 kW. In addition, the High heating value (HHV) can be obtained from Khat waste is 19.38 MJ/kg, and low heating (LHV) can be 18.12 MJ/kg. We have been able to realize it using the Institute of Gas Technology formula based on ultimate analysis. The results show that Khat produces more oil than other wastes. As a result, all Khat waste is naturally occurring and Khat waste usually contains less nitrogen and no sulfur when used as fuel, which is an air pollutant reducing and protecting the environment.

Carbon core–shell Pt nanoparticle embedded porphyrin Co-MOF derived N-doped porous carbon for the alkaline AEM water electrolyzer application

Efficient and durable carbon core-shell Pt@Co nanoparticles interconnected porous carbon electrocatalyst for anion exchange membrane water electrolyzer application.

Hydrogenation, without H2(g)

Industrial hydrogenation requires high temperatures and high pressures of H2(g), which is sourced from fossil fuels. Membrane reactors enable hydrogenation reactions to occur at ambient conditions by sourcing hydrogen from water instead of H2(g). The reactor operates by separating the electrochemical generation of protons in water from the hydrogenation of unsaturated chemicals in the adjacent hydrogenation compartment. These two compartments are separated by a palladium foil that acts as a cathode, a membrane, and a catalyst. This presentation will summarize our work demonstrating a wide range of substrates that can be hydrogenated upon control of the use of different solvents, electrolytes, current densities, and catalysts. I will highlight the potential to hydrogenate substrates in our reactor that are of consequence to a future renewable energy infrastructure. Figure 1

Iot and AI-Based MPPT Techniques for Hybrid Solar and Fuel Cell

Rising electricity demands necessitate advancements in energy generation efficiency and reliability, prompting increased interest in hybrid systems integrating solar and fuel cell renewable sources. This study proposes the MPPTs (maximum power point tracking) approach utilizing Internet of Things (IoT) for actual-time data collections from diverse sensors in a hybrid system. Sensors measuring solar irradiance, temperature, fuel cell performance, and energy storage levels feed data into AI algorithms, dynamically optimizing operational settings for enhanced energy extraction. The AI algorithms adapt to environmental changes, component deterioration, and energy consumption fluctuations, resulting in a continuous efficiency improvement. The proposed technique demonstrates superior tracking accuracy, boosting energy conversion efficiency from 88% to 94%, with a reduced system response time from 4.5 to 2.25 min. Performance metrics validate the effectiveness, suggesting potential for advanced control strategies in future renewable energy systems. Further research avenues include scalability, robustness, and cost-effectiveness for broader real-world applications.

Time-dependent structural tuning of in-situ-grown MnCo2O4 nanostructures on Ni-foam for efficient overall water splitting

The development of highly efficient, stable, and cost-effective electrocatalysts for overall water splitting (OWS) is necessary for future renewable and clean energy systems. In this study, MnCo nanostructures were directly grown on Ni foam by a one-step hydrothermal synthesis method for various reaction times, such as 3, 6, 12, and 24 h, and the synthesized samples are denoted as MnCo-3,11MnCo-3 – The MnCo2O4 samples calcined at 400 °C for 3h. MnCo-6,22MnCo-6 – The MnCo2O4 samples calcined at 400 °C for 6h. MnCo-12,33MnCo-12 – The MnCo2O4 samples calcined at 400 °C for 12h. and MnCo-24,44MnCo-24 – The MnCo2O4 samples calcined at 400 °C for 24h. respectively. The scanning electron microscopy (SEM) images confirm the influence of the reaction time on the morphology of the MnCo nanostructures. The synthesized electrocatalysts reveal good catalytic activity for hydrogen evolution reaction (HER) as well as oxygen evolution reaction (OER) in 1.0 M KOH. The MnCo-24 electrocatalyst outperformed the other prepared electrocatalysts, with Tafel slopes of 140 and 148 mV dec−1 and low overpotentials of 200 and 178 mV for the OER and HER, respectively, at a current density of 10 mA cm−2. The MnCo-24 electrocatalyst exhibited excellent stability for up to 24 h for OWS. This work provides a facile strategy for preparing low-priced electrocatalysts with high catalytic activity.

Interfacial Electron Potential Well Facilitates the Design of Cobalt Phosphide Heterojunctions for Hydrogen Evolution.

The interfacial electron modulation of electrocatalysts is an effective way to realize efficient hydrogen production, which is of great importance for future renewable energy systems. However, systematic theory-guided design of catalysts in heterojunction coupling is lacking. In this work, a multi-level theoretical calculation is performed to screen optimal candidates to form a heterojunction with CoP (101) surface for electrocatalytic hydrogen production. To overcome the weak adsorption of H+ on CoP (101), rational design of electrons potential well at the heterojunction interface can effectively enhance the hydrogen adsorption. All p-type cobalt-based phosphides are considered potential candidates at the beginning. After screening for conductivity, stability, interface matching screening, and ΔGH* evaluation, the CoP/Co2P-H system is identified to be able to display optimal hydrogen production performance. To verify the theoretical design, CoP, CoP/Co2P-H, and CoP/Co2P-O are synthesized and the electrochemical analysis is carried out. The hydrogen evolution reaction (HER) performance is consistent with the prediction. This work utilizes the electron potential well effect and multi-level screening calculations to design highly efficient heterojunction catalysts, which can provide useful theoretical guidance for the rational design of heterojunction-type catalysts.

The role of energy storage in Great Britain's future power system: focus on hydrogen and biomass

The increasing deployment of variable renewable energy (VRE) in the power sector, such as wind and solar photovoltaic, is expected to reduce emissions. However, VRE poses challenges due to their intermittency and variability. The Future Renewable Energy Performance into the Power System Model (FEPPS) is used to analyse VRE penetration in Great Britain's (GB) power system, resulting in VRE shares of around 65%, far from over 80% forecasted by 2030 and 2040 due to flexibility and stability constraints. The incorporation of new power generation and energy storage technologies is also analysed, including the role of biomass and hydrogen as low-carbon fuel options. Hydrogen is assumed to be produced from the curtailment of VRE and biomass. Between 6.57 and 283 Mt. of biomass would be required to balance bio‑hydrogen requirements by 2040; therefore, hydrogen production would depend on technology prioritisation. Power generation from hydrogen technologies (fuel cells and turbines) has a significant role in power system decarbonisation, with hydrogen turbines meeting up to ∼10% of annual electricity demand. In contrast, generation from batteries, adiabatic compressed air energy storage and natural gas turbines is not significant. Moreover, between 7.7 and 9.7 TWh of hydrogen storage would be needed, and there is substantial potential for salt cavern storage in GB, providing many 100s TWh. Finally, incorporating new generation and storage technologies relies on levelized cost of electricity (LCOE) reductions of 31% and 13% by 2030 and 2040, respectively, to keep the power sector on track towards decarbonisation.

Direct regeneration of spent lithium-ion batteries: A mini-review

Recycling spent lithium-ion batteries (LIB) has emerged as a pressing necessity for addressing resource shortages and mitigating environmental pollution. This article reviews the most advanced spent LIBs recycling technology, namely direct regeneration. Traditional recycling methods have problems with high energy consumption and secondary pollution. In contrast, direct regeneration extends battery life by repairing degraded cathode materials and retains battery energy to the maximum extent. This method includes technologies such as chemical lithiation and recycling of waste graphite. These innovative technologies improve battery cycle life, reduce energy consumption, production costs, and promote sustainable resource management. Direct regeneration technology provides new opportunities to reduce resource waste and reduce environmental pollution. It will promote the sustainable management of spent LIBs and support the development of future renewable energy technologies.

Power Consumption Minimization of a Low-Cost IoT Data Logger for Photovoltaic System

This paper introduces an innovative IoT-based data logger for photovoltaic (PV) system monitoring, emphasizing low power consumption and affordability. The system comprises a PV panel, a charging controller, and a backup battery, focusing on monitoring their voltages and currents through a network of voltage and current sensors. Data is stored on an SD card and displayed in real-time on a web server. The FireBeetle 2 ESP32-E microcontroller, chosen for its efficient deep-sleep mode power management, is central to the data logger's design. This study employs several low-power strategies, notably reducing supply voltage and CPU frequency to decrease power consumption significantly. A data buffering mechanism stores sensor readings in the microcontroller's flash memory, transferring them to the SD card hourly to balance power efficiency and data security. Wi-Fi connection intervals are optimized to 45 seconds, balancing power use and system monitoring frequency. The data logger averages a power consumption of 122.78 mW and demonstrates its efficacy against the commercial DI-145 model. Priced at C$ 55.05, the system is both cost-effective and scalable, capable of monitoring multiple PV panels and batteries, reducing per-unit costs. The study underscores the successful integration of affordability, low-power operation, and efficient monitoring in a PV system data logger, showcasing its potential in future renewable energy research.

Strategy planning for sustainable biodiesel supply chain produced from waste animal fat

The rapid depletion of fossil fuels and the adverse environmental effects of combustion has growing concerns about the sustainability of fossil fuel and the world turns its focus on cleaner energy sources. Animal fat-based biodiesel is a sensible alternative to non-renewable energy sources as it's produced from inedible, affordable, and easily available biomass. The effective design of a biodiesel supply chain is pivotal for making it more advantageous than fossil fuels from a social, economic, and environmental standpoint. This model reduces the biodiesel supply chain cost while minimizing its environmental impact and maximizing its social impact. It is planned to have multi-supply sites for providing beef, chicken, and mutton fat, and numerous biorefineries, rendering factories, and distribution depots. The augmented ϵ-constraint approach combined with the lexicographic optimization technique is used to solve the multi-objective optimization model. A numerical example is analyzed, and the results show that the environmental impact of establishing biodiesel production facilities is 99.5% of the environmental impact of the entire biodiesel supply chain and that the cost of installing biodiesel production plants is 99% of the cost of the entire biodiesel supply chain. A heterogeneous fleet of trucks is incorporated to distribute the material throughout the supply chain whereby the impact of transportation on the environment is 0.0007% of the total environmental impact of the biodiesel supply chain and transportation cost amount to 0.0014% of the total costs of the biodiesel supply chain. The model demonstrates the biodiesel supply chain network under a sustainability framework and addresses the environmental and social impact based on the life cycle assessment which accelerates the transition to a renewable energy future.

Price Formation and Multi-Market Synergistic Strategies for Renewable Electricity under the Concept of Life Community

Abstract This paper constructs the price formation mechanism of renewable electricity with three pricing mechanisms, namely, system marginal price (SMP), zonal marginal price (ZMP) and nodal marginal price (LMP), on the basis of electricity price location signal. Through the objective function of the constructed optimal dispatch model of renewable energy power, we calculate the minimum operating cost of each power plant under multi-market synergy and use the C&CG column constraint method to solve the price fluctuation caused by the uncertainty of wind and light. Finally, the multi-market synergy rate of the model is estimated by combining the 500 kV, 300 kV and 200 kV unified wind turbines invested by a provincial grid company in China. The results show that the multi-market coordination coefficients α of wind power and photovoltaic projects are 0.1148 and 0.2849, respectively, and the renewable electricity decreases from 0.355 Yuan/kWh to 0.298 Yuan/kWh in 2025 under the optimal dispatch model of electricity. This paper has certain theoretical value and practical reference significance for future renewable energy power price reform.

Experimental study of the change of pore structure and strength of granite after fluid-rock interaction in CO2-EGS

Deep geothermal energy plays an essential role in the future renewable energy supply. To investigate the mechanism of fluid-rock interaction on the pore structure and the strength of granite in various zones of CO2-EGS, this paper carried out experiments on the fluid-rock interaction at 240 °C and 37 MPa for the three zones of CO2-EGS. Experiments were conducted to test and analyze the elemental composition, surface micromorphology, pore structure, P-wave velocity, and mechanical strength of the granite before and after the fluid-rock interaction. The results show that the water/ScCO2 dissolves the albite and potassium feldspar in the granite, and the carbon in ScCO2 may be sequestered in rock as carbonate mineral precipitates. The injection of ScCO2 fluids increases the pore volume of the rock by 43%, 65%, and 45% in the presence of no water, a small amount of water, and a large amount of water, respectively. There is no chemical reaction, but only physical interaction, between ScCO2 and granite when no water is involved in the core zone of CO2-EGS. The dissolution and precipitation are slight when there is limited water in the rock of surrounding zone, and the P-wave velocity and uniaxial compressive strength also only decrease by ignorable amounts. Only with sufficient water in the outer zone, the ScCO2 fluid can significantly change the uniaxial compressive strength and elastic modulus of rock sample. The findings indicate that the CO2-EGS won't suffer from the blockage of mineral precipitation, decrease of permeability and short service lifetime of geothermal reservoir, which are big problems in conventional water based EGS.

Climate influence on compound solar and wind droughts in Australia

Solar photovoltaic and wind power are central to Australia’s renewable energy future, implying an energy sector vulnerable to weather and climate variability. Alignment of weather systems and the influence of large-scale climate modes of variability risks widespread reductions in solar and wind resources, and could induce grid-wide impacts. We therefore systematically analyse the relationship between compound solar radiation and wind speed droughts with weather systems and climate modes of variability over multiple time scales. We find that compound solar and wind droughts occur most frequently in winter, affecting at least five significant energy-producing regions simultaneously on 10% of days. The associated weather systems vary by season and by drought type, although widespread cloud cover and anticyclonic circulation patterns are common features. Indices of major climate modes are not strong predictors of grid-wide droughts, and are typically within one standard deviation of the mean during seasons with the most widespread events. However, the spatial imprints of the teleconnections display strong regional variations, with drought frequencies varying by more than ten days per season between positive and negative phases of climate modes in some regions. The spatial variability of these teleconnection patterns suggests that droughts in one region may be offset by increased resource in another. Our work highlights the opportunity for minimising the impact of energy production variability by utilising weather and climate intelligence. Exploiting the spatial variability associated with daily weather systems and the seasonal influence of climate modes could help build a more climate-resilient renewables-dominated energy system.

Property rights arrangement and stakeholder networks hindering floating photovoltaics: A case of private open-access ponds in Taiwan

The floating photovoltaic (FPV) system is being promoted in Taiwan to help transition towards zero nuclear energy production. One plan involved installation on 300 artificial irrigation reservoirs in Taoyuan. It was stalled several times and eventually stopped. Through semi-structured group interviews, observation, transcribed audio-taped meetings, and document analysis, a stakeholder network was constructed to understand the institutional arrangement and find the root causes of the problem. The study investigated the network of resource users and managers including solar power companies, central energy provider, pond owners, informal users, environmental groups and government agencies. Apart from inadequate stakeholder participation, it was found that the problem was exacerbated by the private open-access arrangement and the complex governance structure, which made property rights unclear. These considerations highlight the importance of transaction costs associated with social dynamics and multilevel governance, which must be considered in feasibility studies. This paper also argues that initiatives viewed as a failure can nevertheless pave the way for future renewable energy projects by legally clarifying property rights and offering test sites for an objective FPV cost-benefit assessment, which can inform analytic-deliberative discourse for exploring potential compromise solutions. The lessons here can inform similar projects abroad, especially in land-scarce water-rich territories.

Future renewable energy targets in the EU: Impacts on the German transport

The transport sector is at the center of discussions on accelerating the energy transition due to its still increasing contribution to greenhouse gas emissions worldwide; therefore, the EU has set binding targets for the use of renewable energy in transport through the Renewable Energy Directive. To analyze the economic impact of these targets, we developed an optimization model that considers bio- and electricity-based fuel options, various transport sectors, and future policy requirements. Our study of the German transport sector found that imported alternative fuels play a key role in reducing fossil fuel usage. We also identify two technological and managerial obstacles: policymakers need to prioritize the rapid electrification of vehicles in the near future; and in the distant future, more attention is needed in research for new technologies in commercial transport. Although our findings are tailored to Germany, the employed approach can be transferred to other models and countries.