Power Generation Methods Research Articles

A newly developed renewable energy driven multigeneration system with hot silica sand storage for power, hydrogen, freshwater and cooling production

In this study, a renewable energy-driven multigenerational system is designed to produce useful outputs, such as electricity, cooling, freshwater, and hydrogen by applying energy, and exergy methodologies to evaluate its performance. The multigenerational system aims to make use of solar and wind energy in combination with electric heating to heat up the sand particles and gravity-fed them through a pressurized fluidized bed heat exchanger heating and pressurizing the air and producing electricity by spinning the gas generator. The leftover steam is used for absorption cooling and freshwater production through desalination which is partially used to produce hydrogen. The produced energy can be stored in the form of sand and used whenever necessary for the community. Another important aspect to consider is the system economics, which makes silica sand a potential storage option as its stable and inexpensive at 35–45$/ton. The multigenerational system uses renewable energy sources and stores energy with the use of a thermal energy storage material and reduces the environmental impact by emitting much less carbon emissions compared to other power generation and storage methods. The overall energy and exergy efficiencies of the multigenerational system are found as 53.3% and 49.8%, respectively.

A fuel-free self-powered sensor based on photoelectrochemical water/oxygen circulation for ultra-selective detection of levofloxacin

Although self-powered sensors (SPSs) have shown favorable application prospects as portable devices for on-site monitoring of pollutants, it remains challenging to address issues with fuel consumption in these devices as well as improve the detection selectivity. Herein, we constructed a fuel-free SPS for selective detection of levofloxacin (LEV) that was assembled with an In2S3/In2O3 photoanode and molecularly imprinted (MIP) functionalized cathode. Under illumination, the photogenerated holes oxidated water to oxygen at the photoanode, while the photogenerated electrons migrated to the cathode through the external circuit such that the oxygen was re-reduced to water at the cathode. Based on the resulting water/oxygen circulation between the two electrodes, the SPS had a maximum power density of 14.5 μW/cm2 without added fuel or sacrificial agents. Moreover, the molecularly imprinted polymer immobilized on the cathode allowed for specific recognition of LEV. Due to the steric hindrance effects of the MIP and the anti-interference performance from the cathode detection, the photocurrent significantly decreased with increasing LEV concentration. Under the optimum condition, the SPS responded linearly to LEV concentrations in the range of 0.1–107 nM with a detection limit of 0.047 nM and exhibited desirable stability, reproducibility, and selectivity. This work demonstrates a clean and green power generation method for portable sensors.

Data-driven modeling of power generation for a coal power plant under cycling

Increased penetration of renewables for power generation has negatively impacted the dynamics of conventional fossil fuel-based power plants. The power plants operating on the base load are forced to cycle, to adjust to the fluctuating power demands. This results in an inefficient operation of the coal power plants, which leads up to higher operating losses. To overcome such operational challenge associated with cycling and to develop an optimal process control, this work analyzes a set of models for predicting power generation. Moreover, the power generation is intrinsically affected by the state of the power plant components, and therefore our model development also incorporates additional power plant process variables while forecasting the power generation. We present and compare multiple state-of-the-art forecasting data-driven methods for power generation to determine the most adequate and accurate model. We also develop an interpretable attention-based transformer model to explain the importance of process variables during training and forecasting. The trained deep neural network (DNN) LSTM model has good accuracy in predicting gross power generation under various prediction horizons with/without cycling events and outperforms the other models for long-term forecasting. The DNN memory-based models show significant superiority over other state-of-the-art machine learning models for short, medium and long range predictions. The transformer-based model with attention enhances the selection of historical data for multi-horizon forecasting, and also allows to interpret the significance of internal power plant components on the power generation. This newly gained insights can be used by operation engineers to anticipate and monitor the health of power plant equipment during high cycling periods.

Transmissive Labyrinthine Acoustic Metamaterial-Based Holography for Extraordinary Energy Harvesting.

Conventional energy sources are continuously depleting, and the world is actively seeking new green and efficient energy solutions. Enormous amounts of acoustic energy are dissipated daily, but the low intensity and limited efficiency of current harvesting techniques are preventing its adoption as a ubiquitous method of power generation. Herein, a strategic solution to increase acoustic energy harvesting efficiency using a specially designed metamaterial is implemented. A scalable transmissive labyrinthine acoustic metamaterial (LAM) is designed, developed, and employed to maximize ultrasound (40 kHz) capture over its large surface area (>27 k mm2), which is focused onto a piezoelectric film (78.6 mm2), thus magnifying incident sound pressure by 13.6 times. Three different piezoelectric films - two commercial and one lab-made nanocomposite film are tested with LAM in the acoustic energy harvesting system. An extraordinary voltage gain of 157-173% and a maximum power gain of 272% using the LAM compared to the case without the LAM are achieved. Multipoint focusing using holographic techniques, showcasing acoustic patterning to allow on-demand simultaneous harvesting in separate locations, is demonstrated. Our versatile approach for high-intensity acoustic energy harvesting opens future opportunities to exploit sound energy as a resource to contribute toward global sustainability.

The Shockley–Queisser limit and the conversion efficiency of silicon-based solar cells

According to some estimates, every hour, Sun provides planet Earth with more energy than humankind consumes in a whole year. Part of this energy has been essential to ensure living conditions (warmth and food production, for instance) and, more recently, to generate electricity by means of atmospheric (aeolian) and/or geographical (hydropower) sources. In addition to these, the direct (photovoltaic PV) conversion of solar radiation into electricity represents a very elegant method of power generation that causes minimum (or no) environmental disturbance. As a result, numerous efforts have been dedicated to further advance the achievement of clean and sustainable electricity, as supplied by the PV science and technology. Within this scenario, the association of PV with the silicon (Si) semiconductor industry played a crucial role–either by introducing new scientific insights or by promoting successive costs reductions in the field of renewable energy conversion. Yet, the performance of these so-called (either crystalline or amorphous) Si-based solar cells was always a matter of concern. In fact, the subject gained attention with the seminal work by Walter Shockley and Hans Queisser that, in 1961, proposed a model according to which the maximum efficiency of a solar cell is determined by both the solar spectrum and the properties of the semiconductor material. Since then, the work by Shockley and Queisser (also known as the Shockley–Queisser limit) has experienced some improvements and became a reference in the field. Motivated by these facts, along with the main scientific–technological achievements they provided, the Shockley–Queisser limit and the conversion efficiency of the Si-based solar cells along the last 60 years form the basis of this paper.

Near-superhydrophobic silicone microcapsule arrays encapsulating ionic liquid electrolytes for micro-power storage assuming use in seawater

Micro-energy storage, which is convenient for combination with energy harvesting, is known to be realized by microencapsulation with various shell materials, its application is limited to land. Here, we succeeded in fabricating a silicone microcapsule array encapsulating an ionic liquid electrolyte that can store minute power in NaCl solution as well as a minute power generation method. The ArF excimer laser-irradiated silicone rubber underneath silica microspheres was photochemically and periodically swelled by the photodissociation of silicone. Accompanied by the microswellings, the lower molecular weight silicones generated were ejected along a curvature of each the microsphere to enclose the microspheres. After the chemical etching, the silicone microcapsule arrays became hollow. Moreover, each the hollow silicone microcapsule could entrap an ionic liquid in a vacuum. In addition, the silicone microcapsules before and after the encapsulating ionic liquid showed a superhydrophobic or near-superhydrophobic property. As a result, the silicone microcapsule arrays could be confined in a uniform air gap of electrically insulated region in NaCl solution. This means that each the silicone microcapsule encapsulating ionic liquid as electrolytes enables to function as an electric double layer capacitor for micro-power storage, aiming to connect with Internet of Things devices that work under seawater.

A Power Forecasting Method for Ultra-Short-Term Photovoltaic Power Generation Using Transformer Model

The volatility of solar energy, geographic location, and weather factors continues to affect the stability of photovoltaic power generation, reliable and accurate photovoltaic power prediction methods not only effectively reduce the operating cost of the photovoltaic system but also provide reliable data support for the energy scheduling of the light storage microgrid, improve the stability of the photovoltaic system, and provide important help for the optimization operation of the photovoltaic system. Therefore, it is an important study to find reliable photovoltaic power prediction methods. In recent years, researchers have improved the accuracy of photovoltaic power generation forecasting by using deep learning models. Compared with the traditional neural network, the Transformer model can better learn the relationship between weather features and has good stability and applicability. Therefore, in this paper, the transformer model is used for predicting ultra-short-term photovoltaic power generation, and the photovoltaic power generation data and weather data in Hebei are selected. In the experiment, the prediction result of the transformer model was compared to the GRU and DNN models to show that the transformer model has better predictive ability and stability. Experimental results demonstrated that the proposed Transformer model outperforms the GRU model and DNN model by a difference of about 0.04 kW and 0.047 kW in the MSE value, and 22.0% and 29.1% of the MAPE error. In addition, the public DC competition dataset is selected for control experiments to demonstrate the general applicability of the transformer model for PV power prediction in different regions.

A systematic review of the techno-economic assessment of various hydrogen production methods of power generation

Hydrogen is a low or zero-carbon energy source that is considered the most promising and potential energy carrier of the future. In this study, the energy sources, feedstocks, and various methods of hydrogen production from power generation are comparatively investigated in detail. In addition, this study presents an economic assessment to evaluate cost-effectiveness based on different economic indicators, including sensitivity analysis and uncertainty analysis. Proton exchange membrane fuel cell (PEMFCs) technology has the most potential to be developed compared to several other technologies. PEMFCs have been widely used in various fields and have advantages (i.e., start-up, zero-emissions, high power density). Among the various sources of uncertainty in the sensitivity analysis, the cost estimation method shows inflationary deviations from the proposed cost of capital. This is due to the selection process and untested technology. In addition, the cost of electricity and raw materials, as the main factors that are unpredictable.

Prospects for Nuclear Energy in the Framework of Implementation of the Sustainable Development Concept

The need to create an effective global energy structure that meets the current challenges of sustainable development and the growing demand for electricity implies an expansion of the use of nuclear energy potential and necessitates a more detailed study of the prospects for its development, taking into account the economic efficiency, high technology, environmental friendliness and safety of this method of power generation. The purpose of the study is to reveal features of, and identify opportunities for, the global nuclear power development in the conditions of increasing influence of the concept of sustainable development. The study substantiates the necessity and identifies features of the global nuclear power development, characterized by both intensification of capacity obsolescence processes and high degree of their significance in the overall energy structure under conditions of decarbonization. Given the need to achieve the Sustainable Development Goals, the authors justify the necessity of development and implementation at national levels of financial and economic measures in the design of energy markets, taking into account clean energy and the attributes of energy security of lowcarbon technologies, including nuclear energy, supported by the expansion of direct and indirect state participation. The issues considered have practical significance and social implications in terms of solving socioeconomic and environmental problems.

A Design and Analysis of Artificial Vortex (ArVo) Based Stand-Alone Microgrid on Universities of Bangladesh

The administration of universities of Bangladesh faces financial drawbacks every year due to subsidies in the field of electrical power cost. The reason behind this is, students, consume more electric power than they pay for. To solve this problem a modern hydroelectric technology can be implemented. This new technology is called Artificial Vortex (ArVo). This method of power generation utilizes the natural tendency of whirlpool formation of fluids by defining a new flow path thereby causing a turbulent circular movement of fluid layers. Every day students and teachers use a huge volume of water. This water is excluded from the university premises by the wastewater paths. This wastewater flow can be utilized to generate electricity. In Artificial Vortex, a curved wall is designed in the water path to concentrate the water flow and generate rotational energy. This rotational energy is implemented on a vertical axis turbine. This turbine is rotated by the water flow impact and produce clean electricity through an alternator. The cost of electricity has been simulated by HOMER Pro software.

Short-Term Prediction Method of Solar Photovoltaic Power Generation Based on Machine Learning in Smart Grid

In order to improve the accuracy of ultra short-term power prediction of the photovoltaic power generation system, a short-term photovoltaic power prediction method based on an adaptive k-means and Gru machine learning model is proposed. This method first introduces the construction process of the model and then builds a short-term photovoltaic power generation prediction model based on an adaptive k-means and Gru machine learning models. Then, the network structure and key parameters are determined through experiments, and the initial training set of the prediction model is selected according to the short-term photovoltaic power generation characteristics. And the adaptive k-means is used to cluster the initial training set and the photovoltaic power on the forecast day. The Gru model is trained on the initial training set data of each category, and the generated power is predicted in combination with the trained Gru model. Finally, considering three typical weather types, the proposed method is used for simulation analysis and compared with the other three traditional photovoltaic power generation single prediction models. The comparison results show that the proposed short-term photovoltaic power generation prediction method based on an adaptive k-means and Gru network has better effect, better robustness, and less error.

Performance Improvement of Power Transmission Networks Under Stressed Condition

The traditional method of power generation is not capable of fulfilling the increasing electricity demand. The main objective of installing DG is to put less pressure on the centralized power system and to install them nearer to the load or the consumer. DG and FACTS devices allocation in distribution systems has proved to improve the system performance that is the reduction in power loss, improved voltage profile, controlled power transfer capability, active and reactive power flow, improved transient and dynamic stability, better voltage regulation, etc. In this paper, simultaneous allocation of DG and UPFC is done by using an exhaustive search technique to study the impact on the system in overloading conditions. The main objective is to minimize losses. The power flow analysis is done by using the Newton Raphson method and tested on the IEEE 3-bus system and IEEE 14-bus system in Mi Power 9.0 software.

1 MW OTEC 플랜트의 키리바시 계통 연계 해석

Kiribati is an archipelagic state in the central Pacific Ocean. Its grid system operates independently like Korean grid system. Kiribati strives to provide renewable resources to reduce its dependence on fossil fuel imports. It uses solar energy as their principal renewable energy; however, a power generation method using ocean thermal gradient (OTEC) is considered more suitable for the country. In this article, the generator and PV systems were modeled, and then the entire system was simulated and analyzed before adding a new 1 ㎿ OTEC plant to the Kiribati system. After that, the simulated effective power and frequency values were verified to compare the values before and after the installation of the new 1 ㎿ OTEC power plant. In addition, the impact on the system was analyzed if the installed OTEC plant is not connected to the system due to a failure or repair. In conclusion, this study has proved that the load burden of the installed generator is reduced and the operation is stable in case Kiribati operates the OTEC plant.

Solar Tracking System for Efficient Power Generation using Image Processing

Abstract: The emerging era is energy demanding and there is a drastic need to shift to more eco-friendly methods for power generation. The non-renewable resources generate 86% of the world's power. Around the world, coal and gas power plants are shutting down due to lack of fuel. With more and more industries and governments joining the Green Movement, the demand for cost effective renewable energy has surged. Solar power can be easily harnessed as compared to other eco-friendly methods, using solar panels and the conventional process of converting solar energy to electrical energy. Even though solar power is used in domestic as well as industrial applications, it is still a long way from making an impact on environmental change. Since solar panels can only be utilized up to their full potential in clear sky and sunny conditions, its usage is affected by geographic location. However, even in conditions of optimal usage, it's not used up to its full potential. As all solar panels are fixed due sunny side, they receive direct sunlight only for half a day. During the other half, the sun is behind the panels and hence, the power generation drops steadily. To rectify this, we introduce the principle used by sunflowers. Sunflowers intake energy by following the sun from sunrise till sunset. Using the same concept, we make the panels follow the sun resulting in significant increase in energy production. We take images of the sun throughout the day, using image processing make sure that the sun is directly incident to the panels, follow this procedure till sunset. This method makes sure that even in conditions of slight to moderate overcast weather, the sun will be directly incident on panels, such is the power of image processing. The status of the device as well as energy generated can be easily visualized using the mobile application provided. The system can further be made cost effective by modifying the existing panels. The concept is simple yet very efficient, robust and scalable. This method can be used for domestic as well as industrial applications. In optimal conditions, this method can generate up to 40% more energy than a conventional solar panel system of the same size. In conclusion, just by learning from nature, using only a gearbox and controller in addition to panels, we can generate significantly more electricity. In addition to this method, using most efficient solar panels that give maximum power output even in overcast conditions can further increase power output.

Assessment of Different Deep Learning Methods of Power Generation Forecasting for Solar PV System

An increase in renewable energy injected into the power system will directly cause a fluctuation in the overall voltage and frequency of the power system. Thus, renewable energy prediction accuracy becomes vital to maintaining good power dispatch efficiency and power grid operation security. This article compares the one-day-ahead PV power forecasting results of three models paired with three groups of weather data. Since the number, loss, and matching problem of weather data will all influence the training results of the model, a pre-processing data framework is proposed to solve the problem in this study. The models used are a deep learning algorithm-based artificial neural network (ANN), long short-term memory (LSTM), and gated recurrent unit (GRU). The weather data groups are Central Weather Bureau (CWB), local weather station (LWS), and hybrid data (the combination of CWB and LWS data). Compared to the other two groups, hybrid data showed a 5–8% improvement in measurements. In addition, when it comes to different weather conditions, the advantages of the LSTM model were highlighted. After further analysis, the LSTM model combined with hybrid data showed the most accurate measurements, which was proved through forecasting results for one month. Finally, the results indicate that when the amount of data is limited, using hybrid data and the five weather features is helpful for training the model. Accordingly, the proposed model shows better one-day-ahead PV forecasting.

Impact of proximity to thermal power plants on housing prices: Capitalizing the hidden costs of air pollution

This study explores the impact of the Taichung Thermal Power Plant in Central Taiwan on the housing prices of surrounding houses and employs empirical data to capitalize the hidden costs of air pollution. This power plant is the fourth largest in the world and operates mainly through coal burning, in addition, the power plant is still under expansion. The operation of such a large-scale power plant influences the surrounding environment. To reasonably evaluate the cost of thermal power generation, this study capitalizes the impact of proximity to thermal power plants on housing prices and proposes evidence indicating that proximity to thermal power plants has a significant effect on decreases in housing prices, and the closer the distance to the thermal power plant, the more housing prices fall. Additionally, the impact of proximity to the thermal power plant on housing prices is greater in areas with high housing prices. The presence of the thermal power plant within 15 km resulted in housing prices dropping by approximately 25%. This study proposes including negative externalities in the costs of thermal power generation to accurately reflect in the power generation costs and energy prices. This would incentivize power plant operators to invest in more environmentally friendly power generation methods and relieve the burden shared by residents living in proximity to the power plant.

Short-term Forecasting Method of Solar Photovoltaic Power Generation Based on Organic Rankine Cycle

Short-term Forecasting Method of Solar Photovoltaic Power Generation Based on Organic Rankine Cycle

Short-term wind speed prediction based on FEEMD-PE-SSA-BP.

As one of the renewable energy power generation methods, wind power generation shows a high growth trend. However, while wind power is connected to the grid, the volatility and instability of wind power make the power system produce a lot of uncertain fluctuations, which greatly affects the power quality and jeopardizes the stable operation of the power system. Therefore, high wind speed forecasting accuracy can provide a solid basis for grid management, improve the power system's ability to consume wind power, and ensure the safety and stabilization of the power system. In order to solve the problem of inaccurate prediction caused by the non-linearity and unsteadiness of wind speed series, this paper proposes a Fractal Ensemble Empirical Mode Decomposition (FEEMD)-Permutation Entropy (PE)-Sparrow Search Algorithm (SSA)-Error Back Propagation (BP) neural network method for short-term wind speed prediction. This method first uses FEEMD to decompose the original wind speed in order from high to low frequency; then calculates the entropy value of each component, and merges the components with similar entropy values to effectively reduce the computation; and finally, the new sub-series are predicted by SSA-BP model, and the predicted value of the merged new sub-sequences are accumulated to obtain the final wind speed prediction results. The simulation study shows that the proposed prediction model is not only fast and accurate, but also suitable for short-term wind speed prediction.

An easy and efficient power generator with ultrahigh voltage for lighting, charging and self-powered systems

Can you imagine that when you slide on the polymer plate with your hands, the generated electromagnetic field can drive 28-watt fluorescent tube lamps and can even charge the mobile phone? The progress of science and technology subverts traditional cognition. Here, an easy and efficient power generator (EEPG) with ultrahigh voltage is invented, which can light up 28-watt fluorescent tube lamps. The average output voltage and current of planar EEPG can reach up to nearly 5000 V and 116 μA, respectively. Not only can it be used for lighting, but also for charging, and even for mobile phones. The rotary EEPG can be used to harvest wind energy and tidal energy, which overcomes the shortcomings of traditional electromagnetic wind power generation working only when the wind speed is greater than 5 m/s. In addition, the EEPG power generator can be utilized as a footswitch and trajectory tracking tool. This is likely to become an easy and effective method of power generation in the world. It can be expected that mobile phones can be charged by just rubbing their backs. Collecting the green wind energy more easily and efficiently will be truly realized.

A Detailed Study on Power Generation using Speed Breakers

Today we see many vehicles on road creating pollution and using its mechanical energy only for transportation purpose, but if we use that kinetic energy of vehicles to convert into some useful electrical energy then we can use that energy for street lights and can save at least some amount of electrical energy. In this article various methods of generating power using the speed breaker are listed and studied carefully. Many authors conducted many experiments on each type of power generation method, and the results are noted down here. The methods listed here are rack and pinion method, roller speed breaker, crankshaft and piston mechanism, hydraulic speed breaker.