Electricity Generation Research Articles

Integrated Functionality of Photothermal Hydrogels and Membranes in Solar Water, Chemical, Mechanical, and Electrical Domains

AbstractSolar energy can be harnessed and converted into heat via the photothermal effect, which can then be utilized to drive many other reactions to produce important resources, such as water, fuel, electricity, and even mechanical actuation in a clean and sustainable manner. Hydrogels and membranes coupled with photothermal materials are particularly suitable for this purpose because they possess advantageous properties, such as porosity and adaptability. These properties allow for the introduction of diverse additives and functionalities, ensuring that photothermal systems can be customized for specific tasks, thereby enhancing their overall performance, functionality and versatility. This review aims to provide an overview of recent developments and the significance of employing photothermal hydrogels and membranes in multiple fields ranging from clean water, fuel production, electricity generation to mechanical actuation, followed by a discussion on key considerations in materials design and engineering. Finally, the review addresses the challenges and future directions of photothermal applications.

Experimental implementation of extremum-seeking control: Gas fuel efficiency at electrical generation under power requirements

A current challenge stands for operating the emergency power system (EPS) that involves the challenge of supplying sufficient electrical energy at same time the fuel consumption is minimum. A complication arises as power requirements change during the EPS operation which can be seen as uncertain load disturbances. In such a context, the extremum-seeking (ES) is alternative towards the efficient energy conversion when control goal involves fuel optimization along the time operation. Here, the dynamical response of a gas fueled power plant is identified via Hammerstein model. The model is realized such that an ES control is designed for automatically reaching an extreme in face to distinct (unmeasured and uncertain) power requirements. The ES control is designed and experimentally tested at an electrical generator at distinct power requirements. The results show the minimum gas fuel consumption remains in face to distinct power requirements.

Simulation of 1 MWe hybrid solar power plant by the use of nano-fluid with eccentric backup system.

In past years, concentrated solar power (CSP) with an energy backup system has been a unique renewable energy utilization system among intermittent renewable energy systems. It could allow a CSP plant to operate as a base load system in the future. This paper simulates a solar power plant for 1 MWe. Parabolic trough collector (PTC) array and linear Fresnel reflector (LFR) field attached consecutively to produce superheated steam at 40MPa. The Rankine cycle has been used to run the steam turbine and an electricity generator is attached to a steam turbine to convert mechanical energy into electrical energy. Maximum temperature attained at the turbine inlet is 418.13 ˚C in 12:00-13:00 time slot in the month of January. Results show that solar power plant is feasible to produce 1 MWe. The minimum value of the power produced by the generator is 1.01 MWe in November in the 10:00-11:00 time slot whereas the maximum value of generated power is 1.57 MWe in December in the 11:00-12:00 time slot. The overall efficiency of power generated by the Rankine cycle is 21.25% in January in the 10:00-11:00 time slot. An energy storage system is attached to the system to work at night hours or in cloudy weather conditions.

The efficient strategy of operating the active cooling systems incorporated with photovoltaic panels for the three climatic regions: A case study

ABSTRACT Photovoltaic stations in Algeria are considered an effective solution to overcome the problem of its lack of electrical power and achieve the goals of sustainable development. The weather in Algeria is continental, it has three main climate zones from north to south, and the climate in Algeria is desert, which causes an increase in the temperature of photovoltaic modules and thus a decrease in their production of electrical power. Then the active cooling systems that use water as a cooling medium represent a good option to reduce the PV module temperature and thus increase electrical power generation rates. Due to the change in weather in Algeria from north to south and during the four seasons, in some periods, the operation of cooling systems is ineffective and leads to a decrease in the net output electrical power of photovoltaic stations. Therefore, this manuscript aims to develop an advanced strategy for operating active cooling systems incorporated with PV stations to achieve the highest net electrical power output from PV stations in Algeria. To achieve this goal, the theoretical study was conducted on three different regions in Algeria that express its continental climate, namely, Algiers (36°46′ N, 3°03′ E), northern Algeria, and Djelfa (34°40′ N 3°15′ E) in central Algeria and Ghardaïa (32°29′ N 3°40′ E) in southern Algeria, to get the optimal cooling strategies that achieve the highest net electrical power produced by PV stations. The simulation results presented that the average improvement in the electrical power generated from the PV panels incorporated with active cooling systems reached 15.1%, 17.8%, and 19.7% under the climate conditions of Algiers, Djelfa, and Ghardaïa, respectively. The optimal operating strategies of active cooling systems integrated with PV stations in Algeria increase the average net output electrical power of the PV stations by a rate reached 7.65%, 7.13%, and 4.91% for Algiers, Djelfa, and Ghardaïa, respectively.

An integrated policy approach for sustainable decarbonization pathways of energy system in a city under climate change scenarios

An integrated assessment model has been developed to estimate useful energy demand, followed by an optimization model for planning the energy supply. Four scenarios have been developed. The findings of this study highlight the impact of behavioral, climate change, and socio-economic factors on energy demand and supply. Specifically, these factors can lead to significant variations in demand, such as a more than 3.48-fold difference in cooling demand or a 1.75-fold difference in the transportation demand by cars across various scenarios.The use of a dual-fuel car and electric motorcycle is suggested in all scenarios. Waste processing has been done with a landfill in all scenarios except in the final years of the net-zero emission target scenario, where an incinerator is proposed. The supply model for electricity generation in the early years suggests the combined cycle power plant followed by solar energy at the end. However, in the low-cost electricity scenario, it is always suggested to import electricity. The sensitivity analysis showed that the emission penalty and electricity price are the most important parameters. The results of this study will help policymakers and decision-makers in energy system planning and provide researchers with appropriate information, tools, and methods for future research.

Modeling Nuclear-Centric Scenarios for Ukraine’s Low-Carbon Energy Transition Using Diffusion and Regression Techniques

This study presents a mathematical model for forecasting the development of Ukraine’s Integrated Power System (IPS) until 2040, with a specific focus on the expansion of nuclear energy as a cornerstone of the nation’s low-carbon transition. The model is an extension of Frank Bass’s mixed influence diffusion model, incorporating both economic and technological factors. These factors are treated as stochastic variables essential for accurately predicting the evolution of an integrated energy system, particularly in the context of rapid renewable energy sources (RES) growth. The model employs regression techniques using generalized logistic curves, improving forecasting efficiency by aligning modeling parameters with experimental data. The study’s results indicate the potential for optimizing IPS components, including nuclear and thermal power generation, through the model’s application. The model is distinguished by its inclusion of economic and technological impacts, such as state matrices, control actions, and external influence matrices, which enhance the accuracy of simulations and predictions. The validation of the model, based on scenarios of electricity consumption and generation, shows significant alignment with observed trends, confirming the model’s reliability. The findings suggest that this model is an effective tool for developing and refining energy system scenarios, with nuclear energy playing a pivotal role in Ukraine’s sustainable energy future.

Effects of Offshore Wind Farms: Environmental and Social Perspectives from Uruguay

The installation of offshore wind farms is rising, driven by the goal of changing the global energy matrix. However, many of their possible impacts are still unknown. Increased noise levels, disruptions to food chains, pollution due to traffic, and impacts on fishing communities and tourism are all potential effects to consider. Marine habitats are essential carbon dioxide sinks. Therefore, losing marine biodiversity due to offshore wind farms can be counterproductive in mitigating climate change. Balancing biodiversity conservation, wind potential, and political interests is challenging. Today, Uruguay has significantly decreased the fossil share in its electricity generation, incorporating electricity generation from wind, solar, and biomass energy alongside hydroelectricity. In line with this, the country’s Hydrogen Roadmap highlights green hydrogen as relevant, potentially serving as a fuel for both domestic and export transportation. Combining the country’s strong base of wind energy production experience with its sustainable policy, it plans to implement offshore wind farms to produce green hydrogen, making studies of its impacts crucial. This paper reviews the current social and environmental information on the Uruguayan coastal habitat, analyzes onshore wind farms’ ecological studies, and examines offshore wind farms’ global environmental and social impacts. Finally, it proposes studies for environmental approval of offshore wind farms.

Ferroelectric Nanomaterials for Energy Harvesting and Self‐Powered Sensing Applications

AbstractThe rapid development of the Internet of Things has introduced new challenges for miniaturized, highly integrated energy harvesters and sensors, promoting the exploration of various novel nanomaterials. Ferroelectric nanomaterials, characterized by large remanent polarization, exceptional dielectric properties, outstanding chemical stability, and diverse electricity generation capabilities, are emerging as promising candidates in a variety of fields. Possessing various mechanisms for electricity generation, including piezoelectric, pyroelectric, photovoltaic, and triboelectric effects, ferroelectric nanomaterials demonstrate their capability for harvesting and sensing multiple energies simultaneously, including light, thermal, and mechanical energies. This capability contributes to the miniaturization and high integration of electronic devices. This article reviews recent achievements in ferroelectric nanomaterials and their applications in energy harvesting and self‐powered sensing. Different categories of ferroelectric nanomaterials, their ferroelectric properties, and fabrication methods are introduced. The working mechanisms and performance of ferroelectric energy harvesters and self‐powered sensors are described. Additionally, future prospects are discussed.

Integrating renewable energy communities and Italian UVAM project through renewable hydrogen chain

Renewable energy communities (RECs) in Italy could play an important role in increasing the stock of electric renewable generation in the coming years. Their impact on the electric grid could be non-negligible. At the same time with increased electric generation from renewables, a greater need for electric national grid balancing is expected, preferably based on zero-emission energy storage. Based on currently existing entities in Italian regulation framework (i.e. REC and UVAM project), the present work proposes an original business model to create a storage of dispatchable renewable hydrogen to be used for balancing National electric grid. Hydrogen production is from excess of renewables in RECs constituted in the same area. UVAM project allows to access the ancillary services market with the minimum capacity of 1 MW that appears appropriate to the proposed scenario. Based on current technical-economic constraints of the technologies involved (electrolysis and both fuel cell and internal combustion engine fed by hydrogen), proposed business model is not day-present economic feasible: the return on investment is never positive. The cost forecasts available for 2035 indicate that the business model will be likely sustainable, with a net present value becoming positive for configurations with more than 3000 people involved in the RECs, with a photovoltaic penetration condition of 1.8 kWp/capita. This research work suggests an original business model for managers of RECs with an excess of renewables generation.

Discovery of genes that positively affect biomass and stress associated traits in poplar.

Woody biomass serves as a renewable resource for various industries, including pulp and paper production, construction, biofuels, and electricity generation. However, the molecular mechanisms behind biomass traits are poorly understood, which significantly curtails the speed and efficiency of their improvement. We used activation tagging to discover genes that can positively affect tree biomass-associated traits. We generated and screened under greenhouse conditions a population of 2,700 independent activation tagging lines. A total of 761 lines, which had significantly and positively affected at least one biomass-associated trait, were discovered. The tag was positioned in the genome for forty lines which were affected in multiple traits and activation of proximal genes validated for a subset. For two lines we fully recapitulated the phenotype of the original lines through overexpression. Moreover, the overexpression led to more pronounced and additional improvements, not observed in the original lines. Importantly, the overexpression of a Fasciclin-like gene (PtaFLA10) and a Patatin-like gene (PtaPAT) was found to substantially improve biomass, with a 40% increase in dry-stem weight, and enhance drought tolerance, respectively. Additionally, PtaPAT overexpression increased cellulose content, which is crucial for biofuel production. Our work shows that the activation tagging approach applied even on a non-genome saturation scale in a poplar tree can be successfully used for the discovery of genes positively modify biomass productivity. Such dominant forward genetics approaches can aid in biotechnological manipulation of woody biomass traits and help unravel the functions and mechanisms of individual genes, gene families, and regulatory modules.

Global status and prospects for hybrid hydrogen-natural gas systems for power plants in Sub-Saharan Africa

Abstract With the lowest power access rate in the world (51.4%), Sub-Saharan Africa is experiencing a severe energy crisis. Many of the region’s countries report access rates of less than 20%. Even though Sub-Saharan Africa has the lowest global greenhouse gas emissions, the region still suffers from climate change, especially extreme droughts. Efforts to tackle these issues by implementing a macro-grid system that integrates natural gas and renewable energy resources have not been successful in reducing the adverse environmental effects and energy poverty. This study highlights research on the technological approaches used in hybrid hydrogen/natural gas in heavy-duty dual-fuel power plants, their benefits and drawbacks, and their economic viability. The goal of this is to suggest an improved and more reliable hub energy system for Sub-Saharan Africa. While all countries in Sub-Saharan Africa utilize natural gas plants, only 17% are involved in hydrogen production, and none have implemented hybrid methods for electrical energy generation. Studies using experimental and numerical analyses have shown that adding hydrogen to natural gas plants increases overall efficiency and lowers CO2 emissions. Furthermore, this research introduces an energy hub approach that incorporates carbon capture and power-to-X technologies, potentially improving efficiency by 42%. These strategies not only support environmental sustainability but also provide economic advantages by decreasing operational and financial losses in power plants. The results reveal a new pathway for the region’s transition to sustainable energy: identifying key locations for the technological and economic viability of hybrid hydrogen/natural gas power plants in Sub-Saharan Africa.

Exploring Nigeria’s waste-to-energy potential: a sustainable solution for electricity generation

Abstract This research explored the potential of waste-to-energy (WtE) technology as a sustainable solution to Nigeria’s energy deficit and waste management challenges. Various WtE technologies were reviewed, including incineration, anaerobic digestion, gasification, and pyrolysis, highlighting their applicability and benefits for Nigeria. The potential energy yield from different waste streams, combined with economic viability, environmental benefits, and social impacts, demonstrates the importance of WtE technology for the country. The lower heating value of municipal solid waste and agricultural residue significantly affects energy yield. The incineration of 27.36 million tons of organic waste annually while using relevant technology with energy recovery could generate between 14.52 and 23.08 TWh of electricity per annum. The inclusion of paper and textiles increases the potential yield to 18.69 and 29.71 TWh per year. The potential power generation from agricultural residues is estimated at 80.3 GW. However, Nigeria must address technical, economic, and policy challenges to realize this potential. This can be achieved by developing a robust regulatory framework, fostering public–private partnerships, enhancing local capacity, engaging communities, and investing in research and development. The implementation of WtE projects will facilitate sustainable waste management, improve energy security, create jobs, and promote environmental stewardship.

Design of a positive energy district: A Nigerian case study

The potential of possible solutions for the design and implementation of a 50-house Positive Energy District (PED) in Southern Nigeria is presented in this study. Using a transient energy systems simulation software (TRNSYS 18), the energy demand of a typical building is estimated. Different passive design options considering building materials, window sizes, building orientation, roof ventilation are studied to determine the most energy efficient building envelope. TRNSYS and PVSyst (another energy simulation software), are also used to simulate the dynamics of a positive energy building and operation of solar energy systems used for electricity generation, domestic hot water heating and space cooling for Lagos weather conditions. In terms of electric energy balance, the total annual electricity demand is 598,000 kWh while the electricity generated by PV is 728,600 kWh in a year. As regards to site energy balance, annually, the district exports more electricity than it imports. The difference is 110,200 kWh. These results show that in such climatic conditions, despite economic poverty, it is possible to implement the modern PED principles. However, first, it is necessary to reduce energy consumption as much as possible, including solar passive solutions to the building architecture.

The Impact of Abrupt Sunlight Reduction Scenarios on Renewable Energy Production

To combat global warming, energy systems are transitioning to generation from renewable sources, such as wind and solar, which are sensitive to climate conditions. While their output is expected to be little affected by global warming, wind, and solar electricity generation could be affected by more drastic climatic changes, such as abrupt sunlight reduction scenarios (ASRSs) caused by nuclear war (“nuclear winter”) or supervolcanic eruptions (“volcanic winter”). This paper assesses the impacts of an ASRS on global energy supply and security in a 100% renewable energy scenario. National generation mixes are determined according to roadmaps for a global transition to renewable energy, with wind and solar contributing a combined 94% of the global energy supply. Wind and solar generation are determined for a baseline climate and an ASRS following a large-scale nuclear exchange. While effects vary by country, overall wind and solar generation are expected to reduce by 59% in the first year following an ASRS, requiring over a decade for full recovery. Ensuring sufficient energy for everyone’s critical needs, including water, food, and building heating/cooling, would require international trade, resilient food production, and/or resilient energy sources, such as wood, geothermal, nuclear power, tidal power, and hydropower.

Predictive modeling of membrane reactor efficiency using advanced artificial neural networks for green hydrogen production

The imperative to decarbonize the energy sector has prompted substantial advancements in clean electricity generation, with hydrogen emerging as a promising low-carbon energy carrier. While hydrogen synthesis from renewable sources is crucial, challenges persist, necessitating innovative approaches for efficient and sustainable production. This study leverages diverse artificial neural network (ANN) models to assess and predict system efficiency based on key operational variables in membrane reactor systems. The multilayered perceptron (MLP) and radial basis function (RBF) methodologies are employed, with the MLP models optimized across twelve training algorithms and eight activation functions, exploring up to three hidden layers with variable neuron counts. The MLP model, utilizing the Levenberg-Marquard training algorithm and Tangent-Sigmoid activation function, achieved a high correlation coefficient (R2) of 0.9975 for training and 0.9962 for testing, and a mean squared error (MSE) of 0.00425 for training and 0.23951 for testing, indicating precise and accurate efficiency predictions. The Log-Sigmoid activation function also performed well, with R² values of 0.9971 (training) and 0.9961 (testing), and MSE values of 0.004086 (training) and 0.17694 (testing). Optimization of the RBF network identified the best performance with a spread parameter of 1 and 35 neurons, although the MLP model demonstrated superior accuracy and reduced computational time. Statistical analysis, encompassing correlation coefficient, mean squared error, Root Mean Squared error, absolute average deviation, absolute average relative deviation, and runtime, confirms the network’s consistent and accurate estimation of system efficiency across various input variables. The study highlights that applying tansig and logsig activation functions, configured with neuron counts of 20, 17, 6 and 23, 20, 2 at the first, second and third hidden layers, respectively, offers enhanced accuracy and reliability. The MLP model’s high performance underscores its potential to identify optimal conditions for H2 generation based on system efficiency, thereby advancing membrane reactor technology for hydrogen production.

On-chip phonon-enhanced IR near-field detection of molecular vibrations

Phonon polaritons – quasiparticles formed by strong coupling of infrared (IR) light with lattice vibrations in polar materials – can be utilized for surface-enhanced infrared absorption (SEIRA) spectroscopy and even for vibrational strong coupling with nanoscale amounts of molecules. Here, we introduce and demonstrate a compact on-chip phononic SEIRA spectroscopy platform, which is based on an h-BN/graphene/h-BN heterostructure on top of a metal split-gate creating a p-n junction in graphene. The metal split-gate concentrates the incident light and launches hyperbolic phonon polaritons (HPhPs) in the heterostructure, which serves simultaneously as SEIRA substrate and room-temperature infrared detector. When thin organic layers are deposited directly on top of the heterostructure, we observe a photocurrent encoding the layer’s molecular vibrational fingerprint, which is strongly enhanced compared to that observed in standard far-field absorption spectroscopy. A detailed theoretical analysis supports our results, further predicting an additional sensitivity enhancement as the molecular layers approach deep subwavelength scales. Future on-chip integration of infrared light sources such as quantum cascade lasers or even electrical generation of the HPhPs could lead to fully on-chip phononic SEIRA sensors for molecular and gas sensing.

Wavelet CNN‐LSTM time series forecasting of electricity power generation considering biomass thermal systems

AbstractThe use of biomass as a renewable energy source for electricity generation has gained attention due to its sustainability and environmental benefits. However, the intermittent electricity demand poses challenges for optimizing electricity generation in thermal systems. Time series forecasting techniques are crucial in addressing these challenges by providing accurate predictions of biomass availability and electricity generation. Here, wavelet transform is applied for denoising, convolutional neural networks (CNN) are used to extract features of the time series, and long short‐term memory (LSTM) is applied to perform the predictions. The result of the mean absolute percentage error equal to 0.0148 shows that the wavelet CNN‐LSTM is a promising machine‐learning methodology for electricity generation forecasting. Additionally, this paper discusses the importance of model evaluation techniques and validation strategies to assess the performance of forecasting models in real‐world applications. The major contribution of this paper is related to improving forecasting using a hybrid method that outperforms other models based on deep learning. Finally, future research directions and potential advancements in time series forecasting for biomass thermal systems are outlined to foster continued innovation in sustainable energy generation.

Lithium enrichment in high-enthalpy geothermal system influenced by seawater, Indonesia

Lithium holds significant importance in energy storage and can be extracted from geothermal water, making it an additional resource to traditional mining. Combining lithium extraction from geothermal water with electricity generation from geothermal energy offers a compelling strategy to address future energy challenges. Situated in Southeast Sumatra, Indonesia, Way Ratai has considerable geothermal potential and is known for its hot springs. This study primarily aims to characterize lithium enrichment within the Way Ratai geothermal system. Water samples from various sources (one spring, seven hot springs, one well, and seawater) were analyzed for cations, anions, trace elements, and isotopic composition. The results show that Way Ratai is an old geothermal system with sodium-chloride water, where lithium enrichment is attributed to interactions between water and rocks, with some influence from seawater. The discrepancy between calculated and observed magnesium (Mg) concentrations suggests a pre-heating mixing process between rainwater and seawater. The reservoir rock in Way Ratai is mostly andesitic, with temperatures ranging from 182o to 227oC. This study enhances our understanding of thermal water characteristics, reservoir temperatures, and water origins in high enthalpy geothermal systems, offering valuable insights into lithium enrichment in coastal geothermal regions.

Burner and Flame Transfer Matrices of Jet-Stabilized Flames: Influence of Jet Velocity and Fuel Properties

Abstract To achieve the decarbonization of electrical power generation, gas turbines need to be upgraded to combust high-hydrogen content fuels reliably. One of the main challenges in this upgrade is the burner design. A promising burner concept for a high-hydrogen fuel mixture are jet burners, which are highly flashback resistant thanks to their high bulk velocity. Due to its nonacoustically compact extension and the presence of hydrogen in the fuel mixture, new challenges arise in assessing the (thermo)acoustic response of this burner design. A burner transfer matrix (BTM) and the flame transfer function (FTF) or transfer matrix (FTM) are typically measured with the multimicrophone method (MMM) to assess the performance of new burner types in relation to thermoacoustic stability. With the switch toward hydrogen, the fuel/air mixture is significantly altered in its properties regarding the speed of sound and density, which are of fundamental importance for acoustic waves propagation and their reconstruction via the MMM, as highlighted in recent work. In this work, we extend this discussion by studying the influence of the gas composition within the burner when measuring BTMs, and its indirect effect on the assessment of FTFs. Experimentally, we achieve this by adapting the preheating temperature during the measurement of the BTM with a nonreactive mixture in order to match the speed of sound of the hydrogen–air mixture required to flow in the burner under reactive conditions. Additionally, we present an analytical model for the jet burner transfer matrix, which is validated against the experimental data. Since the BTM is fundamental for the assessment of the FTM and FTF, the propagation of the error of changing fuel mixtures in the burner is evaluated. The influence of the variation in reactant composition of the BTM on the FTM assessment is noticeable, particularly in the gain of the FTFs. Furthermore, the influence of the total mass flow and, thus, the bulk flow velocity on the FTF is analyzed.

Augmenting a Concentrated Solar Power (CSP) Plant With a Solar PV Plant

The search for enhanced dispatchability at decreased prices has led to a surge in research on hybrid renewable energy systems. This paper explores the integration of Photovoltaic (PV) technology with Concentrating Solar Power (CSP) plants to enhance energy generation and economic feasibility, focusing on optimising the size of PV augmentation for existing CSP facilities. The study considers CSP plants with both Time-of-Day (ToD) and single tariffs, employing modelling techniques to assess the synergies between these two technologies. The cost of PV technology has significantly reduced, making it a cost-effective choice for electricity generation compared to CSP. CSP-PV augmentation promises to reduce online and offline auxiliary consumption, resulting in enhanced energy generation and a subsequent reduction in energy production costs. This study aims to explore the advantages of combining solar PV with a CSP system and determine the ideal PV size to maximise return, building upon previous studies to assess the technological and economic potential of integrating solar PV with CSP, focusing on South Africa. It utilises a rigorous analysis, combining the System Advisory Model and PVSyst modelling tool within an MS Excel framework. This economic assessment and sizing exercise aims to maximise profits while adhering to the limitations imposed by the power purchase agreement of a 100 MW CSP facility. The optimal size is 15 MWp for the ToD tariff and 16 MWp for the flat tariff. This highlights the potential for CSP-PV augmentation to improve energy dispatchability and financial viability, making it a compelling solution for existing CSP plants.