Power Energy Sources Research Articles

RETRACTED: Elasticity of substitution between clean energy and non-clean energy: Evidence from the Chinese electricity industry

At present, China is in a stage of high-quality economic development. Rising demand for electricity has created a lot of CO2 emissions, which has put great pressure on the low-carbon development of China's power industry. Therefore, China attaches great importance to the potential of various clean power generation to replace thermal power generation. Given this, the study examines the potential for substitution of non-clean energy generation (thermal power generation) and clean energy generation (hydropower, nuclear power generation, and other energy generation) from 1993 to 2022 by using the translog production function and provides a scenario analysis of energy substitution for power generation. Firstly, the k-fold cross-validation method is used for ridge regression estimation in this paper, which avoids the subjective bias caused by the ridge trace diagram method used in most of the previous literatures. Secondly, compared with the previous research on the substitution elasticity of the power sector, this paper subdivides the types of clean power energy when estimating the substitution elasticity, which can better analyze the substitution relationship between thermal power and various clean power. Finally, the estimated substitution elasticity of thermal power and various clean energy sources is greater than 1, which indicates that clean energy generation can effectively replace non-clean energy generation. This provides an effective substitution elasticity parameter for the power sector to study low-carbon development. The scenario analysis show China's power sector can increase the proportion of clean power generation to reduce the carbon emission intensity while ensure power supply, which can help the Chinese government adjust the implementation of policies to promote the early peak of carbon emissions and keep carbon emission at a low level in the power sector.

An applied deep reinforcement learning approach to control active networked microgrids in smart cities with multi-level participation of battery energy storage system and electric vehicles

This study proposed an intelligent energy management strategy for islanded networked microgrids (NMGs) in smart cities considering the renewable energy sources uncertainties and power fluctuations. Energy management of active power and frequency control approach is based on the intelligent probabilistic wavelet fuzzy neural network-deep reinforcement learning algorithm (IPWFNN-DRLA). The control strategy is formulated with deep reinforcement learning approach based on the Markov decision process and solved by the soft actor-critic algorithm. The NMG local controller (NMGLC) provides information such as the frequency, active power, power generation data, and status of the electric vehicle's battery energy storage system to the NMG central controller (NMGCC). Then the NMGCC calculates active power and frequency support based on the IPWFNN-DRLA approach and sends the results to the NMGLC. The proposed model is developed in a continuous problem-solving space with two structures of offline training and decentralized distributed operation. For this purpose, each NMG has a control agent (NMGCA) based on the IPWFNN algorithm, and the NMGCA learning model is formulated based the online back-propagation learning algorithm. The proposed approach demonstrates a computation accuracy exceeding 98%, along with a 7.82% reduction in computational burden and a 61.1% reduction in computation time compared to alternative methods.

Multiparameter Bifurcation Analysis of Power Systems Integrating Large-Scale Solar Photovoltaic and Wind Farms Power Plants

In this paper, we propose a novel codimension-three-parameter bifurcation analysis of equilibria and limit cycles when integrating Renewable Energy Sources (RESs) power plants with an exponential static load model. The study investigates the effect of solar photovoltaic generation margin, wind power generation margin, and loading factor on the local bifurcation of the modified IEEE nine-bus system. The proposed technique considers the real case of the West System Coordination Council (WSCC), the western states of the USA, by using specific models of RES power plants and static loads. The proposed technique helps to create a set of linearly varying parameters for critical operating points of nonlinear systems. The study explores detailed voltage stability analysis through the examination of bifurcation diagrams. The Hopf, limit-induced, and saddle-node bifurcation branches are identified, defining the parameter space’s stable and unstable operational regions. Additionally, the stability regions surrounding the equilibrium points are diligently explored, clarifying the consequences that various bifurcations may exert on these regions. The study offered in this proposed work aids in determining the best ways to monitor and improve these margins while considering system variables and load design.

New energy transmission line fault location method based on Pearson correlation coefficient

The access of new energy power source makes the traditional transmission line structure become complex, and due to the influence of power electronic device control strategy, the fault characteristics have been fundamentally changed, resulting in the traditional transmission line fault localization method can not be applied to the new energy sending line. To address the above problems, this paper analyzes the transient current characteristics of different power supply faults, and learns that there are obvious differences in the transient currents on both sides of the fault point inside and outside the transmission line area, and then proposes a new energy transmission line fault localization method based on the Pearson correlation coefficient, which determines the fault location by calculating the correlation coefficients of the fault waveforms of the neighboring monitoring points. Finally, comparative experiments are carried out under different fault types and fault locations to further verify the applicability of the proposed method.

Assessment and prediction of significant wave height using hybrid CNN-BiLSTM deep learning model for sustainable wave energy in Australia

Wave energy is regarded as one of the powerful renewable energy sources and depends on the assessment of significant wave height (Hs) for feasibility. Hence, this study explores the potential of wave energy by assessing and predicting Hs for two study sites in Queensland (Emu Park and Townsville), Australia. Assessment and prediction of Hs is extremely important for reliable planning, cost management and implementation of wave energy projects. The study utilized oceanic datasets based on wave measurements obtained from buoys along coastal regions of Queensland that are transmitted to nearby receiver stations. The parameters of the datasets include maximum wave height, zero up crossing wave period, peak energy wave period and sea surface temperature to accurately predict Hs. A new hybrid Convolutional Neural Network (CNN) and Bidirectional Long Short Term (BiLSTM) deep learning model with Multivariate Variational Mode Decomposition (MVMD) is developed which is benchmarked by Multi-Layer Perceptron (MLP), Random Forest (RF) and Categorical Boosting (CatBoost) to compare the performance. All models attain relatively high-performance results. The MVMD-CNN-BiLSTM attains slightly better performance values for both study sites among all developed models with highest correlation values of 0.9957 and 0.9986 for Emu Park and Townsville, respectively. Other performance evaluation metrics were also higher for MVMD-CNN-BiLSTM with lowest error values in comparison to the benchmark models. The annual mean of Hs is also computed to compare and obtain an insight with a linear projection. There is a greater ocean wave energy potential for Emu Park for a 10-year period with a projected mean Hs of 0.865 m in comparison to Townsville where the projected mean was of 0.665 m.

Experimental Research of Piezoelectricity as Renewable Energy Power Source for Conveyor Electric Motor on Garbage Collection Vessel

Experimental Research of Piezoelectricity as Renewable Energy Power Source for Conveyor Electric Motor on Garbage Collection Vessel

Meta-reservoir computing for learning a time series predictive model of wind power

Wind energy has become an essential part of the energy power source of current power systems since it is eco-friendly and sustainable. To optimize the operations of wind farms with the constraint of satisfying the power demand, it is critical to provide accurate predictions of wind power generated in the future. Although deep learning models have greatly improved prediction accuracy, the overfitting issue limits the application of deep learning models trained under one condition to another. A huge number of data are required to train a new deep learning model for another environment, which is sometimes not practical in some urgent situations with only very little training data on wind power. In this paper, we propose a novel learning method, named meta-reservoir computing (MRC), to address the above issue, combining reservoir computing and meta-learning. The reservoir computing method improves the computational efficiency of training a deep neural network for time series data. On the other hand, meta-learning is used to improve the initial point and other hyperparameters of reservoir computing. The proposed MRC method is validated using an experimental dataset of wind power compared with the traditional training method. The results show that the MRC method can obtain an accurate predictive model of wind power with only a few shots of data.

Networking Strategies of Triboelectric Nanogenerators for Harvesting Ocean Blue Energy

The utilization of abundant blue energy in the ocean could greatly contribute to achieving carbon neutrality. However, the unsolved economic and technical challenges of traditional technologies for harvesting blue energy have resulted in slow progress. Triboelectric nanogenerators (TENGs), as a new approach for converting mechanical energy into electricity, have great potential for blue energy harvesting, which can be connected as networks with different numbers of units for varying scales of energy harvesting. Here, recent advances of networking strategies of TENGs for harvesting blue energy are reviewed, mainly concerning mechanical and electrical connection designs. Anchoring strategies of devices and networks are also discussed. The development of TENG networks could provide an effective solution for large-scale ocean blue energy harvesting, which can also serve as an in-situ energy station or power source for self-powered systems, supporting various marine equipment and activities.

Energetic Use of Landfill Biogas as a Power Energy Source: An Economic Feasibility Study

This paper presents an economic feasibility analysis for power generation from the energetic use of biogas produced in a landfill. Quantifications were made for the methane generated in the landfill, the electric power generation potential, and carbon credits. A cost analysis study and an economic feasibility analysis were conducted for the proposed system, taking two possible scenarios into consideration: with and without selling carbon credits. This paper shows the importance of including carbon credits in economic feasibility studies conducted in landfill projects, as such credits constitute an important source of income for such projects to become more feasible.

Light-driven soft microrobots based on hydrogels and LCEs: development and prospects.

In the daily life of mankind, microrobots can respond to stimulations received and perform different functions, which can be used to complete repetitive or dangerous tasks. Magnetic driving works well in robots that are tens or hundreds of microns in size, but there are big challenges in driving microrobots that are just a few microns in size. Therefore, it is impossible to guarantee the precise drive of microrobots to perform tasks. Acoustic driven micro-nano robot can achieve non-invasive and on-demand movement, and the drive has good biological compatibility, but the drive mode has low resolution and requires expensive experimental equipment. Light-driven robots move by converting light energy into other forms of energy. Light is a renewable, powerful energy source that can be used to transmit energy. Due to the gradual maturity of beam modulation and optical microscope technology, the application of light-driven microrobots has gradually become widespread. Light as a kind of electromagnetic wave, we can change the energy of light by controlling the wavelength and intensity of light. Therefore, the light-driven robot has the advantages of programmable, wireless, high resolution and accurate spatio-temporal control. According to the types of robots, light-driven robots are subdivided into three categories, namely light-driven soft microrobots, photochemical microrobots and 3D printed hard polymer microrobots. In this paper, the driving materials, driving mechanisms and application scenarios of light-driven soft microrobots are reviewed, and their advantages and limitations are discussed. Finally, we prospected the field, pointed out the challenges faced by light-driven soft micro robots and proposed corresponding solutions.

СОЛНЕЧНЫЕ ЭНЕРГЕТИЧЕСКИЕ СИСТЕМЫ ДЛЯ ЧАСТНЫХ ПОТРЕБИТЕЛЕЙ

Currently, there is an increased interest in the use of combined autonomous power supply systems based on renewable energy sources in the world. Private consumers of the agro-industrial sector need both electric and thermal energy, while installations should not occupy significant land areas. (Research purpose) The research purpose is substantiating the use of photovoltaic systems of increased efficiency for operation in small installations used for private households and farms of the agro-industrial complex. (Materials and methods) Proposed to use a control system based on maximum power point tracking (MPPT) controllers and silicon elements of increased efficiency for more efficient operation of the entire power complex. (Results and discussion) It have been shown that when equipping an autonomous consumer with power plants, reliability and uninterrupted operation of power systems, as well as the necessary power characteristics, become important. It was determined that the daily operation time of such systems is limited by solar insolation and low radiation flux density. It has been established that for the uninterrupted operation of these systems, a reserve of energy or additional power sources is required, and taking into account the variability of solar activity, their conversion factor should be increased by combined generation of thermal and electrical energy. It was found that the modification of the surface of photocells with silver nanoparticles leads to an increase in power characteristics by 20 percent due to the initiation of the effect of surface plasmon resonance. (Conclusions) It was stated that installations for power supply to autonomous consumers of both the agricultural and municipal sectors based on renewable energy sources (in particular, solar installations), modeled using these methods and technologies, make it possible to effectively provide private consumers of the agro-industrial sector of the economy with thermal and electric energy, and when using combined energy conversion, reduce the occupied areas in calculated per unit of power.

Recent progress of high-performance in-plane zinc ion hybrid micro-supercapacitors: design, achievements, and challenges.

With the increasing demand for wearable and miniature electronics, in-plane zinc (Zn) ion hybrid micro-supercapacitors (ZIHMSCs), as a promising and compatible energy power source, have attracted tremendous attention due to their unique merits. Despite enormous development and breakthroughs in this field, there is still a lack of a systematic and comprehensive review to update the recent progress of in-plane ZIHMSCs in the design and fabrication of both micro-anodes and micro-cathodes, the exploration and optimization of new electrolytes, and the investigation of related-energy storage mechanisms. This minireview summarizes the key breakthroughs and recent advances in the construction of high-performance in-plane ZIHMSCs. First, the background and fundamentals of in-plane ZIHMSCs are briefly introduced. Then, new concepts, strategies, and latest exciting developments in the preparation and interfacial engineering of Zn metal micro-anodes, the fabrication of advanced micro-cathodes, and the exploration of new electrolyte systems are discussed, respectively. Finally, the key challenges and future directions for the development of high-performance in-plane ZIHMSCs are presented as well. This review not only accounts for the recent research progress in the field of the in-plane ZIHMSCs, but also provides important new insights into the design of next-generation miniaturized energy storage devices.

A Novel Limited Grid-Forming Strategy to Support Power System Stability under Primary Energy Source Limitations

The available literature on grid-forming converters often assumes an unlimited primary energy source, for example by postulating a constant DC voltage source. However, this assumption is not justified in real-world scenarios as soon as primary energy source limitations are reached. Therefore, it is imperative for grid-forming control strategies to take such limitations into account and ensure that they support robust system stability even under primary source limitations. In this work, a novel concept of limited grid-forming control is introduced that supports the stability of the network, even under primary energy source power as well as energy constraints. Moreover, the control concept also allows for a seamless transition between full grid-forming and limited grid-forming behavior without any control mode switching. The proposed scheme is tested on a modified IEEE 9-bus test system with one synchronous machine replaced by a droop-based grid-forming controlled battery system incorporating the proposed control scheme. The simulation of a couple of dynamic events provides results that demonstrate promising performance, thereby substantiating the feasibility of the proposed control scheme.

Thermodynamic and exergoeconomic assessment of a trigeneration system driven by a biomass energy source for power, cooling, and heating generation

Environmental concerns and the limitations of conventional energy drive the exploration of reliable renewable sources. Herein, a biomass-assisted trigeneration system in conjunction with a methane-fed gas turbine cycle for generating power, cooling, and heating, and an attempt has been made to maximize the energy extraction from the energy source. The proposed system includes a steam Rankine cycle, a gas turbine unit, and a Kalina subsystem, and is investigated from energetic, exergetic, and economic viewpoints. A comprehensive parametric evaluation is carried out, and a multi-objective grey wolf optimization algorithm integrated with the LINMAP decision-maker method is employed to detect the most optimum point through two different scenarios. By conducting a base study, a net output power of 5926 kW, cooling load of 36.56 kW, heating load of 843.4 kW, and an energetic efficiency of 47.41 % can be achieved. The exergetic and economic standpoints bring out an exergy destruction rate of 7697.07 kW, cycle exergetic efficiency of 41.44 %, and a net present value of 12.420 $M that sequels to a payback period of 5.56 years. Biomass proves reliable in multigeneration systems, emphasizing the pivotal role of studies in maximizing energy extraction for diverse product outputs.

Achieving energy independence in urban microgrids: Strategies for domestic resource utilization and environmental sustainability

The quest for energy independence within urban microgrids (MGs) has become increasingly crucial for ensuring domestic resource utilization and environmental sustainability. One of the pivotal challenges lies in the clustering of MGs, a complex task aimed at enhancing their robustness and economic performance during events. However, limited research has been conducted on this intricate clustering problem. This study addresses this gap by proposing an artificial intelligence-based method tailored for MG clustering. In this paper, we introduce a novel multi-objective optimization approach to determine the optimal clustering of standalone MGs. Our methodology considers a range of critical parameters, including distributed generation (DG) droop settings, DG locations and capacities, renewable energy resources from solar units and wind turbines, capacitors, and power transmission lines. By minimizing power losses and enhancing voltage stability, we identify a virtual cut-set line, often referred to as the "virtual line," characterized by minimal power exchange between clustered MGs. To achieve optimal and cost-effective performance, we develop an enhanced differential genetic evolution algorithm designed to address multi-objective problems within stringent constraints. Additionally, we employ Bayesian theory within artificial intelligence to estimate the optimal output power of renewable energy sources, specifically wind turbines and solar panels. To validate the effectiveness of our approach, we apply it to a 69-bus MG across various scenarios. Our findings demonstrate the proficiency of our method in achieving energy independence, domestic resource utilization, and environmental sustainability within urban microgrids.

Energizing Sustainability: Solar Cell Radio Wave AI Transformations in Business and Health, with a Focus on Vaccines and Antibiotics

This review article provides a comprehensive examination of the synergistic integration of several technologies, including radio waves, solar cell technology, artificial intelligence (AI), business information technology (IT), immunizations, and antibiotics, with the aim of fostering sustainable innovation. A comprehensive analysis is carried out, highlighting the connections between smart technology, healthcare, and renewable energy sources. The abstract looks at how solar energy and radio waves might advance technology, with artificial intelligence and business IT aiming to maximize productivity. The evaluation also explores the potentially innovative ways in which vaccinations and antibiotics could enhance public health. By linking different disciplines together, the article aims to shed light on the holistic and long-term strategy that emerges when many technologies come together for the good of business and society. This paper explores the intricate connections between solar cell technology and artificial intelligence (AI), showing how advances formerly unimaginable are propelled by the convergence of advanced computing power and renewable energy sources. When it comes to facilitating the seamless integration of many technologies to optimize productivity and decision-making processes, business IT plays a critical role. The important sector of medicine, where immunizations and antibiotics are critical for creating resilient populations, is also covered in the article. This study explains the potential for comprehensive and long-lasting solutions by combining these technological and medical advancements, while also emphasizing the necessity of multidisciplinary cooperation in tackling the challenges of our rapidly changing environment.

Low‐carbon economic optimal operation strategy of rural multi‐microgrids based on asymmetric Nash bargaining

AbstractThe multi‐microgrids operation can greatly improve the proportion of renewable energy integration and power reliability of rural microgrids through energy trading between adjacent microgrids. Therefore, this paper proposes an optimization method for the low‐carbon economic operation of rural microgrids which contain wind power, photovoltaic, biogas, and other common rural renewable energy sources. Using the asymmetric Nash bargaining model which can form the non‐linear energy mapping function to quantify the bargaining power of each microgrid to establish the multi‐microgrids energy trading model, and the non‐convex trading optimization problem is equivalently transformed into two convex optimization subproblems, subproblem 1 is microgrids alliance costs containing the emission costs, and subproblem 2 is cooperative benefits distribution. Then, the accelerated Alternating Direction Method of Multipliers (ADMM) method is used to solve subproblem 1 and subproblem 2, respectively, and the privacy of transaction information is also protected. The results show that: compared with the single‐microgrid operation, the combined operation of rural multi‐microgrids with biogas can greatly improve the renewable energy consumption rate, reduce carbon emission, and improve the profit income of the alliance and each microgrid. Meanwhile, the accelerated ADMM algorithm used can greatly improve the solution speed.

TECHNOLOGICAL PROGRESS IN SOLAR PANELS: ACHIEVEMENTS AND INNOVATIONS

The modern world faces a major problem of meeting energy needs, which requires us to develop sustainable and environmentally friendly energy sources. Dependence on conventional sources such as coal and oil has a negative impact on the environment and leads to energy insecurity. However, science and technological progress are putting their efforts into developing alternative energy sources, and one of the most promising is solar energy. However, despite the prospects of solar energy, there is a question that requires our attention and resolution: how to achieve the maximum efficiency and practical applicability of solar panels. Despite the great potential of this energy source, there are a number of technological challenges and limitations that must be overcome for its full use. This article aims to provide a detailed analysis of technological developments in the field of solar panels, focusing on their achievements and innovations that have transformed them from simple experimental devices into powerful and affordable sources of sustainable energy. Let's look at the important steps taken to improve the materials, structures and efficiency of solar panels, and consider the innovations that ensure their integration into various areas of life. Solar energy plays a key role in the global search for alternative, sustainable energy sources, helping to reduce greenhouse gas emissions and dependence on conventional sources. One of the main reasons for this breakthrough is technological progress in solar panels, which has transformed them from an experimental device to a powerful and affordable source of energy. This article explores the key advances and innovations that have made solar panels an efficient and attractive solution to today's energy needs.

Design and implementation of an IoT-based educational model for smart homes: a STEM approach

This work presents a comprehensive study on the design and implementation of an educational model for a smart home, leveraging the principles of the Internet of Things (IoT). The model encompasses three key levels: command, communication, and management, and integrates several subsystems, including communication, signalling, lighting control, temperature regulation, garbage container filling, nd sensor data monitoring. The hardware components for implementing the Mini Smart House, a practical application of the model, are detailed in the study. The Mini Smart House utilizes a range of technologies for efficient management and incorporates renewable energy sources for power. The model was independently produced by students participating in a STEM project, with research activities encompassing sketching, construction part creation, sensor assembly, Arduino board programming in the Arduino IDE environment, and system functionality testing. The Mini Smart House was validated within the STEM-center of the Physics and Mathematics Faculty of Ternopil Volodymyr Hnatiuk National Pedagogical University during the educational process and numerous trainings and seminars for pupils and teachers of computer science.

Adaptive model-based optimization for fusion-based metal additive manufacturing (directed energy deposition)

In this paper, a computationally fast adaptive optimization methodology based on a semi-analytical physics-based model is developed for predicting and altering the processing parameters to avoid overheating and improve the dimensional accuracy and uniformity of the print in fusion-based metal additive manufacturing. A unique model-based algorithm is developed, as a closed-form optimization solution, for multi-track/multi-layer printing. The algorithm uses simulated data of the temperature field and geometrical profile of tracks to back-calculate the optimal power of the thermal energy source such that overheating is neutralized, and each track arrives at the desired dimensions. The proposed methodology is adaptive as it predicts the power of the thermal energy source for subsequent tracks based on historical thermal information from previous tracks during the process, regardless of the initial processing parameters. This abstraction offers required speed-ups for computationally fast optimization. The developed adaptive methodology is experimentally tested and validated for directed energy deposition in the present study, while the methodology can be adapted to other fusion-based additive processes using different thermal energy sources such as laser beam, electron beam, and electric arc.