Renewable Power Sources Research Articles

Renewable energy mechanisms: An examination of the progress achieved in wireless power transmission techniques

Technology for “wireless power transfer” (WPT) has advanced greatly in recent years, especially when used for renewable power sources. Environmental issues and the growing need for renewable energy sources have prompted a great deal of study in this area. This report provided an overview of the evolution of WPT technologies for renewable energy systems, including their advantages, disadvantages, and future possibilities. An introduction to WTP’s foundational ideas is provided, including techniques such as resonant pairing, capacitive connection, and microwave-based approaches. It delves into how these systems have been modified and improved to facilitate the long-distance transmission of renewable energy sources like wind and solar power. The study emphasizes the benefits of WPT in renewable energy systems, such as improved convenience, lower maintenance costs, and the opportunity to lessen negative effects on the environment. Problems with WPT technology in renewable energy applications are also analyzed critically. Power loss in transmission, misalignment, and electromagnetic interference are all examples of technical difficulties, and these are on top of the financial and legal obstacles. WPT’s effects on humans and animals are also explored since they must be taken into account before broad use of the technology.

Energy Policy and Climate Change: Challenges for Public Sector Organizations in South Sulawesi

This paper aims to discuss about some issues and prospect that might faced by the power policies for public sector organizations in South Sulawesi. It celebrates the richness of the renewable power sources available within the region and the possibility of the enhanced opportunities brought about through technological advancement, at the same time as identifying strategic limitations including economic limitations, infrastructural challenges, regulatory issues, political considerations, social acceptance concerns, technological challenges amongst others. The paper offers recommendations, including reforms for coverage and financing as well as current funding mechanisms, building an institutional framework, partnership, and integration of climate resilience into the network’s capacity. Al these strategies are needed for providing solutions to the boundaries and for effective implementation of the sustainable energy development in the region which contributes for economic boom, social advancement and overall climatic trade system, mitigation process.

Smart Electricity Meter Monitoring and Prediction using I-Socket

As the world's population and energy needs continue to grow, the demand for power is ever-increasing. This has put a strain on natural resources and renewable sources of power, which are not yet developed enough to operate self-sufficiently. In this context, optimal power sharing and efficient energy consumption is essential. To this end, a smart electricity meter monitoring and prediction system has been developed using i-Socket, a hardware module that tracks the energy consumed by each device in a house and estimates the monthly electricity bill. This system notifies the user when the bill surpasses a set limit and sets the monitoring rate of a connected device to the socket based on the type of device, making it an energy efficient system. Current sensor used is the ACS712 sensor and the microcontroller is Arduino UNO. A Boost converter is used for the power supply and the software application is developed for monitoring electricity bills. This system takes input of present data of current and predicts the meter reading for the following months. However, the same can also be achieved by using a python server for holding an ML model that will be developed to predict future price of the bill based on the current usage of power. This is acts as an affordable alternative to that by using the iSocket

PV ENERGY FORECASTING USING DEEP LEARNING ALGORITHM

Solar energy has vast potential in India which is a rapidly growing economy with diverse geographical features. Solar energy has intermittent behaviour and depends on geographical and weather conditions. Therefore, the reliability of the solar depends on the seamless operation of solar plants with the latest technologies. The main objective of power operator is to facilitate the renewable power sources intergeration for maintaining an uninterrupted power supply. To achieve this objective, researchers have employed various Deep Learning methods of machine learning, such as RNN, LSTM, CNN and SVM for accurate solar power forecasting with higher relibaility. In this paper, a GA-CNN deep learning algorithm is employed with an optimized hyperparameters technique for PV energy forecasting. This technique outperforms when compared with the other methods such as LSTM, KNN-SVM, and CNN-RNN techniques in terms of RMSE, MAE, MSE and R-Square performance indices. This method provides a better and more robust method of deep learning for solar PV energy forecasting.

Design and verification of q-axis perturbation based active islanding detection schemes for DG systems

The penetration of grid integrated distributed generation (DG) in the present decade, has benefited rural communities, the environment, and the power sector. These renewable power sources based DGs could eliminate the need of extensive transmission networks, especially in remote areas, reduce emissions and improve power supply reliability. A significant drawback of grid integrated DG systems is the islanding of DG units, which puts workers' safety at risk and raises the possibility of damaging electrical infrastructure. Therefore, islanding detection techniques are used to reduce the danger associated with islanded functioning of DG units. Fast detection, small non detection area and less power quality disturbance are the major requirements of any islanding detection method. To address this issue of islanding, researchers have proposed various islanding detection strategies. This paper compares various q-axis controller-based islanding identification approaches: sub-harmonic perturbation (SHP), complementary reactive power perturbation (CRPP), and even harmonic perturbation (EHP). In all three proposed methodologies, the perturbations introduced result in frequency deviations surpassing the predefined threshold values. But the time of islanding detection is least in the CRPP approach. CRPP can also drift the total harmonic distortion (THD) beyond the corresponding threshold in an appreciable way. The performance of these (Islanding detection methods) IDMs is evaluated through simulations using MATLAB-Simulink on a PV fed DG. The efficacy of the comparative analysis is ensured with necessary waveforms.

Real-time Assessment of Ride-through Capability of Grid-tied Converter in Renewable Power Applications

This paper presents a comprehensive strategy for controlling grid-tied converters, with the primary goal of seamlessly integrating renewable power sources into the grid while ensuring stability and reliability. The control strategy is designed to achieve unity power factor operation under normal grid conditions, while also adhering to ride-through requirements to withstand voltage dips. Using the Typhoon HIL604 real-time simulation platform, extensive testing of the grid-tied converter is conducted to validate its performance. The results of the performance assessment are thoroughly analyzed and presented to exemplify the efficacy of the proposed control strategy. Notably, the paper emphasizes the converter's operation at unity power factor during normal grid conditions, indicating maximized active power delivery to the grid. Furthermore, the grid-tied converter demonstrates impressive ride-through capability during symmetrical voltage dip, achieved through the dynamic adjustment of grid current references to maintain grid stability. This research contributes to the advancement of grid-tied converter control strategies, offering insights into enhancing the integration of renewable power sources into the power grid while upholding system reliability and stability. Overall, this research offers valuable insights into the practical implementation of grid-tied converters for renewable power integration. It presents a comprehensive control strategy that not only enhances the integration of renewable power sources into the power grid but also prioritizes system reliability and stability.

Reliability and Availability of Renewable Energy Sources in Kenya: A Review

The research sought to evaluate the reliability and availability of renewable energy sources in Kenya by examining the available literature regarding the state of energy production and consumption of the renewable energy sources. Several studies have been conducted to establish the capacity of the renewable electrical power sources including hydroelectric power, geothermal sources, wind power, solar energy and biofuels. Regarding production of electricity, the existing capacity is barely able to keep up with demand. The research used systematic review as the methodology of collecting relevant data for analysis. Given that more than 38% (2021) of Kenya’s electricity comes from hydropower, the situation is particularly difficult during the dry months of the year when water levels are low. Capacity gaps are then compensated by expensive thermal generation based on fossil fuels. Geothermal power has the capacity to be the most significant source of renewable energy with a potential of up to 7000MW. The reliability indices of the renewable energy sources, based on System Average Interruption Frequency Index (SAIFI) are at 1.98 (SAIFI) and at 4.37, based on the Customer Average Interruption Duration Index (CAIDI), which are below the international global standards. The research concluded that increased consumption of electricity, availability of alternate energy sources, amount of restoration time and number of planned interruptions have a significance influence on the reliability of a renewable energy source, with restoration time being the most significant. It is therefore recommended that new technologies and innovations that lead to reduced power interruptions and greenhouse gas emission be adopted.

A new single switch universal supply voltage DC-DC converter for PV systems with MGWM-AFLC MPPT controller.

The present power generation government companies focus on Renewable Power Sources (RPS) because their features are zero carbon footprint, unlimited power source, fewer greenhouse pollutants, fewer output wastages, plus creatinga very healthy atmosphere. In this work, the sunlight source is utilized for the Photovoltaic (PV) standalone network. The merits of sunlight sources are very optimal human resources needed, unlimited natural sources, plus easy operation. However, the solar power resource is nonlinear fashion. As a result, the operating point of the sunlight network fluctuates concerning sunlight intensity. So, in this article, the Modified Grey Wolf Methodology with Adaptive Fuzzy Logic Controller (MGWM-AFLC) is introduced to maintain the operating point of the sunlight system at the global power point position of the PV array. This controller traces the MPP with very low fluctuations in the PV-produced voltage. The advantages of this proposed method arefewer sensing devices required, less difficulty in development, more useful for rapid changes inthe sunlight temperatures, simpler to realize operation, greater economic growth, plus highly useful for household applications. The sunlight set-up generation voltage is lowwhich is improved by introducing the new Wide Power Rating High Voltage DC-DC Boost Converter (WPRHVBC). The features of this WPRHV converter are low voltage strain on semiconductor devices, few passive elements are enough to develop the circuit, plus easy understanding.

Review on Design and Fabrication of Solar Panel Cleaning Mechanism

Today energy demands are increasing sharply therefore as a Renewable source of power, solar energy has an important role in producing the electricity direct from sunlight. The surface of solar panel in solar power plants becomes dirty due to natural factors such as dust and bird droppings. This project aims at increasing the efficiency of solar power plants by solving the problem of accumulation of dust on the surface of solar panel which leads to reduction in plant output and overall plant efficiency. It proposes to develop a Solar Panel Cleaning System which could remove the accumulated dust on its surface on a regular basis and maintain the solar power plant output.[8]

From Rigid to Flexible: Progress, Challenges and Prospects of Thin c‐Si Solar Energy Devices

AbstractThe increasing adoption of solar energy as a renewable power source marks a significant shift toward clean, sustainable alternatives to conventional energy forms. A notable development in this field is the advancement of thin monocrystalline silicon (c‐Si) solar cells. Characterized by their lightweight, flexible nature, these solar cells promise to transform the renewable energy landscape with enhanced durability, adaptability, and portability. Amidst the growing demand for sustainable energy solutions, refining and evolving thin c‐Si solar cell technologies is crucial. This review comprehensively examines the latest progress in thin c‐Si solar energy conversion device technologies, offering an extensive overview of current methodologies for producing thin c‐Si films, advanced light trapping techniques, surface passivation strategies, and methods for managing thin wafers. It also explores the wide‐ranging applications of thin c‐Si solar cells. Furthermore, this article sheds light on the techno‐economic aspects, highlighting the intertwined challenges of commercializing these innovative cells. Through an in‐depth analysis of the latest advancements, this review serves as a valuable resource for researchers and industry experts, keeping them abreast of current trends and catalyzing further advancements in thin c‐Si solar cell technology.

Engineering Carbon Nanotube Yarns with Polyaniline Coating toward Enhanced Thermoelectric Performance.

The growing demand for wearable electronics has driven the development of flexible thermoelectric (TE) generators which can harvest waste body heat as a renewable power source. Despite carbon nanotube (CNT) yarns have attracted significant attention as a promising candidate for TE materials, challenges still exist in improving their TE efficiency for commercial applications. Herein, we developed high performance CNT/polyaniline (PANI) yarns by engineering the coating of polyaniline emeraldine base (PANIeb), in which CNT yarns were firstly coated by PANIeb layer and further doped by HCl vapor treatment. With the incorporation of PANIeb, σ and S were simultaneously increased to 1796 S cm-1 and 74.8 μV K-1 for CNT/PANIeb 4-2d fibers, respectively. Further HCl vapor treatment induced greatly increased σ to 3194 S cm-1, but maintained be 83 % value before doping, giving rise to the highest power factor of 1224 μW m-1K-2, higher than pristine CNT yarns of 576 μW m-1K-2. Combining outstanding high TE performance and bending durability, a flexible TE generator was constructed to deliver high out power of 187 nW with temperature gradients of about 30 K. These results demonstrate the potential promise of high-performance CNT/PANI-HCl yarns to harvest waste body heat for sustainable power supply.

Solar Panel Sun-Rays Tracking and Cleaning System for Improvement in Power Efficiency

There is an increasing need to reduce greenhouse gas emissions and find more environmentally friendly ways to power the planet. An estimated 4 billion years will pass before the sun stops shining, making it a renewable natural power source. A sustainable method of generating electricity from the sun's energy is offered by solar power systems. In comparison with single axis solar tracker, a dual axis solar tracker tracks the sun at every instance hereby increasing the efficiency by 30-35%. In this system we have a solar panel which tracks the position of the sun and always try to remain perpendicular to sun rays. It will track the sun in all four directions i.e east-west and north-south. This paper focus more on Design of Dual tracking system, which includes installation of GPS and RTC system, stored energy usage for application , periodic cleaning in order to maintain efficiency of panel and comparative analysis of stored energy. Alternative, less expensive options have been suggested because the solar tracking equipment is expensive to manufacture.The goal of this effort is to create a solar dual tracking system model that includes solar panel cleaning as well.

Commercial Nafion Membranes for Harvesting Osmotic Energy from Proton Gradients that Exceed the Commercial Goal of 5.0 W/m2.

Osmotic energy from proton gradients in industrial acidic wastewater can be harvested and converted to electricity through membranes, making it a renewable and sustainable power source. However, the currently designed membranes for harvesting proton gradient energy in acidic wastewater cannot simultaneously achieve excellent chemical/mechanical stability and high power density under a large-scale area and require high cost and complex operations. Here, we demonstrate that commercial Nafion membranes with high chemical/mechanical stability and proton transport selectivity can generate a power density of 5.1 W/m2 for harvesting osmotic energy from proton gradients under a test area of 0.2 mm2, which exceeds the commercial goal of 5.0 W/m2. Even under a test area of 12.5 mm2, a power density of 2.1 W/m2 can be achieved under a strong acid condition. In addition, the heat can greatly promote proton transport, and the power density is increased, i.e., 8.1 W/m2 at 333 K (5.1 W/m2 at 293 K) under a test area of 0.2 mm2. By matching membranes with ion selectivity, our work demonstrates the potential of Nafion membranes for harvesting proton gradient energy in acidic wastewater and provides an approach for large-scale conversion of osmotic energy.

Recent advances in copper kesterite thin film photocathodes for hydrogen production via water splitting: A review

The increasing scarcity of fossil fuels has made it crucial to investigate alternative, clean, and renewable power sources. Hydrogen, the simplest element on the planet can act as an energy carrier and can be a promising solution for this quest of carbon-free energy alternates. The practical implementation of cost-effective, efficient, and stable production of hydrogen fuel from water presents a significant challenge. Researchers around the globe have been actively exploring photoelectrochemical (PEC) water-splitting methods for large-scale commercialization of hydrogen production through solar-driven processes. Among these methods, the use of semiconductor photoelectrodes (PEs) is considered highly efficient. In this review, of the effectiveness and stability of the PEs, we describe the recent progress of copper-based chalcogenides for hydrogen generation by water splitting using solar energy. It is anticipated that this report will provide insightful guidance to study the low-cost materials for solar to chemical fuel energy conversion.

Footstep Power Generation Using Piezoelectric Transducer

Abstract: In the modern world, power is becoming more and more crucial for all tasks. Even with our existing array of renewable and nonrenewable power sources, we are still unable to meet our needs for electricity. The population of humans is one of these assets. For this project, jogging or walking is how we produce energy. Calorie burning occurs when you go up and down stairs. People can generate power with their weight when they walk on steps or platforms. The energy produced will be kept in a battery for subsequent use around the house. This technique can be applied in sites with a high pedestrian traffic volume, such as movie theaters, colleges, schools, and temples. Applying pressure causes the control mechanism's piezoelectric sensors to produce electrical energy. The energy produced by walking on the floor is pollution- and noise-free. You may use this technology's energy to charge other electrical devices, such as laptops and mobile phones.

Shading impact on the electricity generated by a photovoltaic installation using “Solar Shadow-Mask”

Abstract Solar energy is an excellent source of renewable power, but designing photovoltaic (PV) systems can be challenging without proper knowledge of solar radiation. The amount of energy received at the installation site plays a crucial role in determining the number of panels required to meet the electrical demand. For a given electrical demand, higher levels of received energy imply a reduced number of panels required, and vice versa. Hence, having knowledge of this irradiance is of paramount importance in the design and sizing of solar energy systems. The primary objective of this article is to provide an accurate estimation of electricity production in a PV installation when it is affected by shading. To achieve this, we performed calculations of the irradiation (direct and diffuse) received by our installation using the “Hottel” method, which integrates relevant local site parameters for our study. Subsequently, we study the impact of shading by conducting shade mask measurements on our installation. This enables us to obtain an accurate estimation of the irradiance received by the PV panels. The measurements include surveys of the geometry of obstacles and shade measurements taken at various times of the day. Additionally, a practical study of shading effects will be conducted using “close shading masks.” This method was applied to a 1.44 kWp PV installation located at the Faculty of Science and Technology of Settat (Morocco), where the output energy of PV panels was calculated. Finally, the effect of shading on the PV installation was quantified.

High-Power-Density Rechargeable Hybrid Alkali/Acid Zn-Air Battery Performance Through Value-Added Conversion Charging.

Rechargeable Zn-air batteries (ZABs) are considered highly competitive technologies for meeting the energy demands of the next generation, whether for energy storage or portable power. However, their practical application is hindered by critical challenges such as low voltage, CO2 poisoning at the cathode, low power density, and poor charging efficiency Herein, a rechargeable hybrid alkali/acid Zn-air battery (h-RZAB) that effectively separates the discharge process in an acidic environment from the charging process in an alkaline environment, utilizing oxygen reduction reaction (ORR) and glycerol oxidation reaction (GOR) respectively is reported. Compared to previously reported ZABs, this proof-of-concept device demonstrates impressive performance, exhibiting a high power density of 562.7mWcm-2 and a high operating voltage during discharging. Moreover, the battery requires a significantly reduced charging voltage due to the concurrent utilization of biomass-derived glycerol, resulting in practical and cost-effective advantages. The decoupled system offers great flexibility for intermittently generated renewable power sources and presents cost advantages over traditional ZABs. As a result, this technology holds significant promise in opening avenues for the future development of renewable energy-compatible electrochemical devices.

Entirely Coupled Recurrent Neural Network-Based Backstepping Control for Global Stability of Power System Networks

In an interconnected power system network, global performance is affected by various unknown power system parameters subjected to system uncertainties. These unknown parameters in the control expression deteriorate the performance of the power system network. Therefore, these terms associated with the control expression must be estimated precisely. This paper proposes an adaptive backstepping scheme using an entirely coupled recurrent neural network (ECRNN) to estimate these control terms. Three continuous differentiable functions are used to design control input, virtual signals, and adaptive control laws to achieve global performance. The objective of ECRNN is to reduce the control complexity by estimating the associated nonlinear term in the control expression. It will facilitate the complex formulation of the controller and improve system performance. The robust functional estimation ability of ECRNN will make the system immune to uncertainties that may appear due to external disturbances. In ECRNN, feedback loops are added to each neuron layer to achieve additional estimation accuracy. The proposed adaptive law enhances the online weight update speed and accuracy. Verification of the proposed scheme is carried out in MATLAB/Simulink. It is also verified in the real-time digital simulator (RTDS) platform using the IEEE standard New England 39-bus, 10-machine power system model. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is motivated by the existing limitations in the global performance of a multimachine power system network due to uncertainty in the system parameters. Uncertainties in the power system network primarily appeared due to penetration of renewable power sources, load uncertainty, or occurrence of an uncertain fault in the network. As power system networks are complex nonlinear interconnected systems, these perturbations in the generator states may cause economic losses, load demand uncertainty, or electric scarcity. Thus, a control scheme is required to address the global performance of such networks. This article proposes uniform ultimate bounded stability of synchronous generators in a multimachine power system network with a robust ECRNN-based backstepping control design. The other available technique has not adequately addressed the global performance under uncertainties. The efficacy of the proposed scheme is verified in a real-time environment via a real-time digital simulator which may be a feasible solution for designing excitation control for the synchronous machine in an extensive industrial network.

The effects of reducing renewable power intermittency through portfolio diversification

Intermittency of renewable power sources is a growing concern for grid reliability in the United States and it has not been properly addressed by public policy for very long. One simple way to mitigate this problem is to diversify the renewable energy portfolio. This work studies the case of Texas, a state that not only is the largest wind power producer in the US, but that also has faced considerable diversification in its renewable energy portfolio in the past ten years. A dispatch model is proposed to simulate how thermal generators respond to changes in the intermittency of renewable sources. Additionally, since higher renewable output usually means higher intermittency and vice-versa, a mean–variance analysis is used to study this trade-off. Results indicate that although intermittency decreases the reliability of the grid, reducing it can have adverse effects as increasing the dispatch of polluting power sources and decreasing the revenues of thermal generators, what can decrease important investment in dispatchable power sources in the long-term. Also, results show that the subsidy design adopted by the federal government is an obstacle to portfolio diversification.

Optimal design of hybrid renewable energy sources with battery storage using an efficient weighted mean of vectors algorithm

An improved algorithm called mINFO is developed, based on the modification of the original weIghted meaN oF vectOrs Optimizer (INFO). In this article, to enhance the original INFO method, an improved version called mINFO is presented by incorporating the sine-cosine function and balancing exploration/exploitation strategy through the control randomization parameter. This suggested mINFO algorithm is applied to obtain the optimal size design of two hybrid renewable power sources (HRPS), the first configuration consists of the photovoltaic (PV)/biomass system (BS)/fuel cells (FC) and the second configuration consists of the PV/BS/battery storage units. This HRPS are designed to generate the power needed to meet the loads supply on the campus of Aswan University in the Sahary region of Aswan city, Egypt. The proposed mINFO approach is developed to avoid the weaknesses of the original INFO method by enhancing the balance between the exploration and exploitation phases and preventing the conventional INFO method from getting stuck in local minima. In order to prove the efficiency and robust search capabilities of the developed mINFO algorithm, the mINFO algorithm is tested by using 10 IEEE CEC’20 test suites and its results are compared with the original INFO method and other well-known optimization algorithms. The proposed mINFO algorithm achieved the first rank in terms of the Friedman mean rank-sum test over F2, F3, F4, F6, F9, and F10, and the second rank after the original INFO algorithm over F1 and F7. Boxplot analysis is also used to show the features of data distribution. For the majority of functions, the boxplots generated by the mINFO algorithm have the narrowest ranges of values out of all comparison algorithms. As a result, for the majority of test functions, the mINFO algorithm shows more promise than its competitors. The main objectives of the suggested HRPS are to minimize energy cost (EC), excess energy (EXE), and maximize the power system's reliability (LPSP). By applying the mINFO algorithm to find the optimal sizing for the two configurations proposed in this study and by comparing the mINFO results with other algorithms such as the original INFO, Particle Swarm optimization (PSO), Runge Kutta optimization (RUN), Sooty Tern Optimization Algorithm (STOA), and Gray Wolf optimization (GWO). The proposed mINFO method shows the best results for the two suggested configurations compared to the other algorithms used by studying the systems for 100 iterations and 50 times run. For the PV/BS/battery units, the best optimal solution has achieved after 36 iterations with the lowest EC with 0.11676275 $/kWh, net present cost (NPC) with 3,420,036 $, EXE with 0.090282 and LPSP with 0.0419687649, while for the PV/BS/FC units, the optimal solution has achieved after 11 iterations with the minimal EC with 0.17996701 $/kWh, NPC with 5,271,318 $, EXE with 0.150022 and LPSP with 0.07598679.