The Spin Torus Energy Model and Electricity

Defining the electron to be a toroidal form of concentrated energy rather than a monopole point-charge, such as used for the Orbital Nuclear Atomic Model (ONAM), leads to a subtly different explanation for electricity and the dynamic nature of electromagnetic fields. The Spin Torus Energy Model (STEM) is used to define the electron and positron, which are then used to explain the nature of electric and magnetic fields, electric current generation from battery and induction sources, capacitor charge and discharge, and superconductivity. STEM supports the notion that free positrons exist within matter, and are equal in importance to electrons: as ONAM makes no provision for positrons within matter, this assertion has wide ranging implications for atomic structure models and chemistry.

Battery Technologies for Grid-Level Large-Scale Electrical Energy Storage

Grid-level large-scale electrical energy storage(GLEES) is an essential approach for balancing the supply–demand of electricity generation, distribution, and usage. Compared with conventional energy storage methods, battery technologies are desirable energy storage devices for GLEES due to their easy modularization, rapid response, flexible installation, and short construction cycles. In general, battery energy storage technologies are expected to meet the requirements of GLEES such as peak shaving and load leveling, voltage and frequency regulation, and emergency response, which are highlighted in this perspective. Furthermore, several types of battery technologies, including lead–acid, nickel–cadmium, nickel–metal hydride, sodium–sulfur, lithium-ion, and flow batteries, are discussed in detail for the application of GLEES. Moreover, some possible developing directions to facilitate efforts in this area are presented to establish a perspective on battery technology, provide a road map for guiding future studies, and promote the commercial application of batteries for GLEES.

Insufficiency of Cellular Energy (ICE) May Precede Neurodegeneration in Alzheimer’s Disease and Be Treatable via the Alternative Cellular Energy (ACE) Pathway

The term neurodegeneration emphasizes the destruction of neuronal cells as the primary explanation of many major neurological illnesses, including Alzheimer’s disease. Specialized functioning of cells requires more cellular energy than is needed for basic cell survival. Cells can acquire energy both from the metabolism of food and from the alternative cellular energy (ACE) pathway. The ACE pathway is an added dynamic (kinetic) quality of the body’s fluids occurring from the absorption of an external force termed KELEA (Kinetic Energy Limiting Electrostatic Attraction). KELEA is attracted to separated electrical charges and is seemingly partially released as the charges become more closely linked. As suggested elsewhere, the fluctuating electrical activity in the brain may attract KELEA from the environment and, thereby, contribute to the body’s ACE pathway. Certain illnesses affecting the brain may impede this proposed antenna function of the brain, leading to a systemic insufficiency of cellular energy (ICE). Furthermore, individual neurons may derive some of the energy for their own activities from the repetitive depolarization of the cell. This may explain why hyper-excitability of neurons can occur in response to cell damage. This adaptive mechanism is unlikely to be sustainable, however, especially if there is a continuing need to synthesize neurotransmitters and membrane ion channels. The energy deficient neurons would then become quiescent and, although remaining viable, would not perform their intended specialized functions. Actual cell death would not necessarily occur till much later in the disease process. The distinction between quiescent and degenerated cells is important since the ACE pathway can be enhanced by several means, including the regular consumption of KELEA activated water. This, in turn, may improve the proposed antenna function of individual neurons, leading to a sustained restoration of specialized function via the ACE pathway. This paper explores this novel concept and provides a rationale for clinical testing of KELEA activated water in patients with neurological and psychiatric illnesses, including Alzheimer’s disease.

Enhancing Renewable Energy Integration:A Gaussian-Bare-Bones Levy Cheetah Optimization Approach to Optimal Power Flow in Electrical Networks

In the contemporary era,the global expansion of electrical grids is propelled by various renewable energy sources(RESs).Efficient integration of stochastic RESs and optimal power flow(OPF)management are critical for network optimization.This study introduces an innovative solution,the Gaussian Bare-Bones Levy Cheetah Optimizer(GBBLCO),addressing OPF challenges in power generation systems with stochastic RESs.The primary objective is to minimize the total operating costs of RESs,considering four functions:overall operating costs,voltage deviation management,emissions reduction,voltage stability index(VSI)and power loss mitigation.Additionally,a carbon tax is included in the objective function to reduce carbon emissions.Thorough scrutiny,using modified IEEE 30-bus and IEEE 118-bus systems,validates GBBLCO’s superior performance in achieving optimal solutions.Simulation results demonstrate GBBLCO’s efficacy in six optimization scenarios:total cost with valve point effects,total cost with emission and carbon tax,total cost with prohibited operating zones,active power loss optimization,voltage deviation optimization and enhancing voltage stability index(VSI).GBBLCO outperforms conventional techniques in each scenario,showcasing rapid convergence and superior solution quality.Notably,GBBLCO navigates complexities introduced by valve point effects,adapts to environmental constraints,optimizes costs while considering prohibited operating zones,minimizes active power losses,and optimizes voltage deviation by enhancing the voltage stability index(VSI)effectively.This research significantly contributes to advancing OPF,emphasizing GBBLCO’s improved global search capabilities and ability to address challenges related to local minima.GBBLCO emerges as a versatile and robust optimization tool for diverse challenges in power systems,offering a promising solution for the evolving needs of renewable energy-integrated power grids.

Electricity as an Energy Vector: A Performance Comparison with Hydrogen and Biodiesel in Italy

This study presents a comparative analysis of electricity, hydrogen, and biodiesel as energy vectors, with a focus on powering an aluminum smelter in southern Italy. It evaluates these vectors in terms of efficiency, land requirements for carbon-neutral energy production, and capital expenditure, providing insights throughout the entire supply chain (upstream, midstream, and downstream) into their feasibility for industrial applications. The research reveals that biodiesel, despite being carbon neutral, is impractical due to extensive land requirements and lower efficiency if compared to other vectors. Hydrogen, downstream explored in two forms as thermal power generation and fuel cell technology, shows lower efficiency and higher capital expenditure compared to electricity. Additionally, green hydrogen production’s land requirements significantly exceed those of electricity-based systems. Electricity emerges as the most viable option, offering an overall higher efficiency, lower land requirements for its green production, and comparatively lower capital expenditure. The study’s findings highlight the importance of a holistic assessment of energy vectors, considering economic, environmental, and practical aspects along the entire energy supply chain, especially in industrial applications where the balance of these factors is crucial for long-term sustainability and feasibility. This comprehensive analysis provides valuable guidance for similar industrial applications, emphasizing the need for a balanced approach in the selection of energy vectors.

Achieving Synergistic Improvement in Dielectric and Energy Storage Properties of All-Organic Poly(Methyl Methacrylate)-Based Copolymers Via Establishing Charge Traps

How to achieve synergistic improvement of permittivity(ε_(r))and breakdown strength(E_(b))is a huge challenge for polymer dielectrics.Here,for the first time,theπ-conjugated comonomer(MHT)can simultaneously promote theε_(r)and E_(b)of linear poly(methyl methacrylate)(PMMA)copolymers.The PMMA-based random copolymer films(P(MMA-co-MHT)),block copolymer films(PMMA-b-PMHT),and PMMA-based blend films were prepared to investigate the effects of sequential structure,phase separation structure,and modification method on dielectric and energy storage properties of PMMA-based dielectric films.As a result,the random copolymer P(MMA-coMHT)can achieve a maximumε_(r)of 5.8 at 1 kHz owing to the enhanced orientation polarization and electron polarization.Because electron injection and charge transfer are limited by the strong electrostatic attraction ofπ-conjugated benzophenanthrene group analyzed by the density functional theory(DFT),the discharge energy density value of P(MMA-co-PMHT)containing 1 mol%MHT units with the efficiency of 80%reaches15.00 J cm^(-3)at 872 MV m^(-1),which is 165%higher than that of pure PMMA.This study provides a simple and effective way to fabricate the high performance of polymer dielectrics via copolymerization with the monomer of P-type semi-conductive polymer.

Analysis of Hybrid Rechargeable Energy Storage Systems in Series Plug-In Hybrid Electric Vehicles Based on Simulations

In this paper, an extended analysis of the performance of different hybrid Rechargeable Energy Storage Systems (RESS) for use in Plug-in Hybrid Electric Vehicle (PHEV) with a series drivetrain topology is analyzed, based on simulations with three different driving cycles. The investigated hybrid energy storage topologies are an energy optimized lithium-ion battery (HE) in combination with an Electrical Double-Layer Capacitor (EDLC) system, in combination with a power optimized lithium-ion battery (HP) system or in combination with a Lithium-ion Capacitor (LiCap) system, that act as a Peak Power System. From the simulation results it was observed that hybridization of the HE lithium-ion based energy storage system resulted from the three topologies in an increased overall energy efficiency of the RESS, in an extended all electric range of the PHEV and in a reduced average current through the HE battery. The lowest consumption during the three driving cycles was obtained for the HE-LiCap topology, where fuel savings of respectively 6.0%, 10.3% and 6.8% compared with the battery stand-alone system were achieved. The largest extension of the range was achieved for the HE-HP configuration (17% based on FTP-75 driving cycle). HP batteries however have a large internal resistance in comparison to EDLC and LiCap systems, which resulted in a reduced overall energy efficiency of the hybrid RESS. Additionally, it was observed that the HP and LiCap systems both offer significant benefits for the integration of a peak power system in the drivetrain of a Plug-in Hybrid Electric Vehicle due to their low volume and weight in comparison to that of the EDLC system.

Inter-stage performance and energy characteristics analysis of electric submersible pump based on entropy production theory

The electric submersible pump(ESP) is a crucial apparatus utilized for lifting in the oil extraction process.Its lifting capacity is enhanced by the multi-stage tandem structure, but variations in energy characteristics and internal flow across stages are also introduced. In this study, the inter-stage variability of energy characteristics in ESP hydraulic systems is investigated through entropy production(EP) analysis,which incorporates numerical simulations and experimental validation. The EP theory facilitates the quantification of energy loss in each computational subdomain at all ESP stages, establishing a correlation between microscopic flow structure and energy dissipation within the system. Furthermore, the underlying causes of inter-stage variability in ESP hydraulic systems are examined, and the advantages and disadvantages of applying the EP theory in this context are evaluated. Consistent energy characteristics within the ESP, aligned with the distribution of internal flow structure, are provided by the EP theory, as demonstrated by our results. The EP theory also enables the quantitative analysis of internal flow losses and complements existing performance analysis methods to map the internal flow structure to hydraulic losses. Nonetheless, an inconsistency between the energy characterization based on EP theory and the traditional efficiency index when reflecting inter-stage differences is identified. This inconsistency arises from the exclusive focus of the EP theory on flow losses within the flow field, disregarding the quantification of external energy input to the flow field. This study provides a reference for the optimization of EP theory in rotating machinery while deeply investigating the energy dissipation characteristics of multistage hydraulic system, which has certain theoretical and practical significance.

CT-NET: A Novel Convolutional Transformer-Based Network for Short-Term Solar Energy Forecasting Using Climatic Information

Photovoltaic(PV)systems are environmentally friendly,generate green energy,and receive support from policies and organizations.However,weather fluctuations make large-scale PV power integration and management challenging despite the economic benefits.Existing PV forecasting techniques(sequential and convolutional neural networks(CNN))are sensitive to environmental conditions,reducing energy distribution system performance.To handle these issues,this article proposes an efficient,weather-resilient convolutional-transformer-based network(CT-NET)for accurate and efficient PV power forecasting.The network consists of three main modules.First,the acquired PV generation data are forwarded to the pre-processing module for data refinement.Next,to carry out data encoding,a CNNbased multi-head attention(MHA)module is developed in which a single MHA is used to decode the encoded data.The encoder module is mainly composed of 1D convolutional and MHA layers,which extract local as well as contextual features,while the decoder part includes MHA and feedforward layers to generate the final prediction.Finally,the performance of the proposed network is evaluated using standard error metrics,including the mean squared error(MSE),root mean squared error(RMSE),and mean absolute percentage error(MAPE).An ablation study and comparative analysis with several competitive state-of-the-art approaches revealed a lower error rate in terms of MSE(0.0471),RMSE(0.2167),and MAPE(0.6135)over publicly available benchmark data.In addition,it is demonstrated that our proposed model is less complex,with the lowest number of parameters(0.0135 M),size(0.106 MB),and inference time(2 ms/step),suggesting that it is easy to integrate into the smart grid.

Renewable Energy:Wind Turbines,Solar Cells,Small Hydroelectric Plants,Biomass,and Geothermal Sources of Energy

In this paper,we present five basic types of renewable energy sources,namely:wind turbines,solar cells,small hydroelectric plants,biomass,and geothermal sources of energy.Wind turbines transform energy of wind into electrical energy,solar cells transform energy of sun into electric energy,hydroelectric plants transform energy of water into electric energy,devices or machines can be constructed to transform energy of biomass into heat energy,and geothermal energy into some form of energy.In this paper we present basic information and reasons why there is need today to use these forms of energy—called green energies,we present how these devices or machines function,and we propose for future work design of typical devices or machines that will satisfy basic functional needs.

Thermal Energy Collection Forecasting Based on Soft Computing Techniques for Solar Heat Energy Utilization System

In recent years, introduction of alternative energy sources such as solar energy is expected. Solar heat energy utilization systems are rapidly gaining acceptance as one of the best solutions to be an alternative energy source. However, thermal energy collection is influenced by solar radiation and weather conditions. In order to control a solar heat energy utilization system as accurate as possible, it requires method of solar radiation estimation. This paper proposes the forecast technique of a thermal energy collection of solar heat energy utilization system based on solar radiation forecasting at one-day-ahead 24-hour thermal energy collection by using three different NN models. The proposed technique with application of NN is trained by weather data based on tree-based model, and tested according to forecast day. Since tree-based-model classifies a meteorological data exactly, NN will train a solar radiation with smoothly. The validity of the proposed technique is confirmed by computer simulations by use of actual meteorological data.

Energy Storage Operation Modes in Typical Electricity Market and Their Implications for China

As the Chinese government proposes ambitious plans to promote low-carbon transition,energy storage will play a pivotal role in China’s future power system.However,due to the lack of a mature electricity market environment and corresponding mechanisms,current energy storage in China faces problems such as unclear operational models,insufficient cost recovery mechanisms,and a single investment entity,making it difficult to support the rapid development of the energy storage industry.In contrast,European and American countries have already embarked on certain practices in energy storage operation models.Through exploration of key issues such as investment entities,market participation forms,and cost recovery channels in both front and back markets,a wealth of mature experiences has been accumulated.Therefore,this paper first summarizes the existing practices of energy storage operation models in North America,Europe,and Australia’s electricity markets separately from front and back markets,finding that perfect market mechanisms and reasonable subsidy policies are among the main drivers for promoting the rapid development of energy storage markets.Subsequently,combined with the actual development of China’s electricity market,it explores three key issues affecting the construction of costsharing mechanisms for energy storage under market conditions:Market participation forms,investment and operation modes,and cost recovery mechanisms.Finally,in line with the development expectations of China’s future electricitymarket,suggestions are proposed fromfour aspects:Market environment construction,electricity price formation mechanism,cost sharing path,and policy subsidy mechanism,to promote the healthy and rapid development of China’s energy storage industry.

Sustainability on Energy Governance: Recent Trends of the Electricity Sector in Azerbaijan

Our aim is to analyze sustainability on energy governance,recent trends of the electricity sector in Azerbaijan,in particular,the degree of efficiency of the electricity system and the tariff structure to give recommendations for future development and perspectives of energy sector development in Azerbaijan.We argue that government policy should be oriented towards identification of those factors that seek energy efficiency for sustainable development,uncover several laws,ensuring energy security,and encourage electricity market.Besides that by comparing electricity tariffs in Azerbaijan with some other European countries,we find advantages in the Azerbaijan-EU partnership on the energy field,thus we propose appropriate forms of cooperation regarding to European Neighborhood Policy.

Communication Networking Schemes for Wide Area Electric Vehicle Energy Service Network

Electric vehicles (EVs) are an emerging type of mobile intelligent power consumption devices in Smart Grid as new green transport tools. In order to provide a powerful automation and intelligence support for wide area electric vehicles energy service network, we analyze the network infrastructure and communications demands of various terminals, devices and monitoring systems distributed in wide area electric vehicle energy service network. According to interactive user services scenarios and energy operations intelligent monitoring, we propose multimode communication integration architecture for wide area electric vehicle energy service network by means of the fusion of the Internet of Things (IoT) technology. Then, we design different networking schemes in access networks and backbone transmission networks meeting multi-scene and multi-operation interaction requirements. The networking schemes will provide efficient technical support to implement intelligent, cross-regional, interactive energy services for electric vehicle users.

Prime Energy Challenges for Operating Power Plants in the GCC

There is a false notion of existing available, abundant, and long lasting fuel energy in the Gulf Cooperation Council (GCC) Countries;with continual income return from its exports. This is not true as the sustainability of this income is questionable. Energy problems started to appear, and can be intensified in coming years due to continuous growth of energy demands and consumptions. The demands already consume all produced Natural Gas (NG) in all GCC, except Qatar;and the NG is the needed fuel for Electric Power (EP) production. These countries have to import NG to run their EP plants. Fuel oil production can be locally consumed within two to three decades if the current rate of consumed energy prevails. The returns from selling the oil and natural gas are the main income to most of the GCC. While NG and oil can be used in EP plants, NG is cheaper, cleaner, and has less negative effects on the environment than fuel oil. Moreover, oil has much better usage than being burned in steam generators of steam power plants or combustion chambers of gas turbines. Introducing renewable energy or nuclear energy may be a necessity for the GCC to keep the flow of their main income from exporting oil. This paper reviews the GCC productions and consumptions of the prime energy (fuel oil and NG) and their role in electric power production. The paper shows that, NG should be the only fossil fuel used to run the power plants in the GCC. It also shows that the all GCC except Qatar, have to import NG. They should diversify the prime energy used in power plants;and consider alternative energy such as nuclear and renewable energy, (solar and wind) energy.

Simulation of District Cooling Plant and Efficient Energy Air Cooled Condensers (Part I)

In hot arid countries with severe weather, the summer air conditioning systems consume much electrical power at peak period. Shifting the loads peak to off-peak period with thermal storage is recommended. Model A of residential buildings and Model B of schools and hospitals were used to estimate the daily cooling load profile in Makkah, Saudi Arabia at latitude of 21.42°N and longitude of 39.83°E. Model A was constructed from common materials, but Model B as Model A with 5 - 8 cm thermal insulation and double layers glass windows. The average data of Makkah weather through 2010, 2011 and 2012 were used to calculate the cooling load profile and performance of air conditioning systems. The maximum cooling load was calculated at 15:00 o’clock for a main floor building to a 40-floor of residential building and to 5 floors of schools. A district cooling plant of 180,000 Refrigeration Ton was suggested to serve the Gabal Al Sharashf area in the central zone of Makkah. A thermal storage system to store the excess cooling capacity was used. Air cooled condensers were used in the analysis of chiller refrigeration cycle. The operating cost was mainly a function of electrical energy consumption. Fixed electricity tariff was 0.04 $/kWh for electromechanical counter, and 0.027, 0.04, 0.069 $/kWh for shifting loads peak for the smart digital counter. The results showed that the daily savings in consumed power are 8.27% in spring, 6.86% in summer, 8.81% in autumn, and 14.55% in winter. Also, the daily savings in electricity bills are 12.26% in spring, 16.66% in summer, 12.84% in autumn, and 14.55% in winter. The obtained maximum saving in consumed power is 14.5% and the daily saving in electricity bills is 43% in summer when the loads peak is completely shifted to off-peak period.

Short-Term Electricity Price Forecasting Using a Combination of Neural Networks and Fuzzy Inference

This paper presents an artificial neural network, ANN, based approach for estimating short-term wholesale electricity prices using past price and demand data. The objective is to utilize the piecewise continuous na-ture of electricity prices on the time domain by clustering the input data into time ranges where the variation trends are maintained. Due to the imprecise nature of cluster boundaries a fuzzy inference technique is em-ployed to handle data that lies at the intersections. As a necessary step in forecasting prices the anticipated electricity demand at the target time is estimated first using a separate ANN. The Australian New-South Wales electricity market data was used to test the system. The developed system shows considerable im-provement in performance compared with approaches that regard price data as a single continuous time se-ries, achieving MAPE of less than 2% for hours with steady prices and 8% for the clusters covering time pe-riods with price spikes.

A Review of Price Forecasting Problem and Techniques in Deregulated Electricity Markets

In deregulated electricity markets, price forecasting is gaining importance between various market players in the power in order to adjust their bids in the day-ahead electricity markets and maximize their profits. Electricity price is volatile but non random in nature making it possible to identify the patterns based on the historical data and forecast. An accurate price forecasting method is an important factor for the market players as it enables them to decide their bidding strategy to maximize profits. Various models have been developed over a period of time which can be broadly classified into two types of models that are mainly used for Electricity Price forecasting are: 1) Time series models;and 2) Simulation based models;time series models are widely used among the two, for day ahead forecasting. The presented work summarizes the influencing factors that affect the price behavior and various established forecasting models based on time series analysis, such as Linear regression based models, nonlinear heuristics based models and other simulation based models.

Improved Short Term Energy Load Forecasting Using Web-Based Social Networks

In this article, we are initiating the hypothesis that improvements in short term energy load forecasting may rely on inclusion of data from new information sources generated outside the power grid and weather related systems. Other relevant domains of data include scheduled activities on a grid, large events and conventions in the area, equipment duty cycle schedule, data from call centers, real-time traffic, Facebook, Twitter, and other social networks feeds, and variety of city or region websites. All these distributed data sources pose information collection, integration and analysis challenges. Our approach is concentrated on complex non-cyclic events detection where detected events have a human crowd magnitude that is influencing power requirements. The proposed methodology deals with computation, transformation, modeling, and patterns detection over large volumes of partially ordered, internet based streaming multimedia signals or text messages. We are claiming that traditional approaches can be complemented and enhanced by new streaming data inclusion and analyses, where complex event detection combined with Webbased technologies improves short term load forecasting. Some preliminary experimental results, using Gowalla social network dataset, confirmed our hypothesis as a proof-of-concept, and they paved the way for further improvements by giving new dimensions of short term load forecasting process in a smart grid.

Machine Learning-based Electric Load Forecasting for Peak Demand Control in Smart Grid

Increasing energy demands due to factors such as population,globalization,and industrialization has led to increased challenges for existing energy infrastructure.Efficient ways of energy generation and energy consumption like smart grids and smart homes are implemented to face these challenges with reliable,cheap,and easily available sources of energy.Grid integration of renewable energy and other clean distributed generation is increasing continuously to reduce carbon and other air pollutants emissions.But the integration of distributed energy sources and increase in electric demand enhance instability in the grid.Short-term electrical load forecasting reduces the grid fluctuation and enhances the robustness and power quality of the grid.Electrical load forecasting in advance on the basic historical data modelling plays a crucial role in peak electrical demand control,reinforcement of the grid demand,and generation balancing with cost reduction.But accurate forecasting of electrical data is a very challenging task due to the nonstationary and nonlinearly nature of the data.Machine learning and artificial intelligence have recognized more accurate and reliable load forecastingmethods based on historical load data.The purpose of this study is to model the electrical load of Jajpur,Orissa Grid for forecasting of load using regression type machine learning algorithms Gaussian process regression(GPR).The historical electrical data and whether data of Jajpur is taken for modelling and simulation and the data is decided in such a way that the model will be considered to learn the connection among past,current,and future dependent variables,factors,and the relationship among data.Based on this modelling of data the network will be able to forecast the peak load of the electric grid one day ahead.The study is very helpful in grid stability and peak load control management.