Autonomous Multi-Factor Energy Flows Controller (AmEFC): Enhancing Renewable Energy Management with Intelligent Control Systems Integration

The transition to sustainable energy systems is one of the defining challenges of our time, necessitating innovations in how we generate, distribute, and manage electrical power. Micro-grids, as localized energy hubs, have emerged as a promising solution to integrate renewable energy sources, ensure energy security, and improve system resilience. The Autonomous multi-factor Energy Flow Controller (AmEFC) introduced in this paper addresses this need by offering a scalable, adaptable, and resilient framework for energy management within an on-grid micro-grid context. The urgency for such a system is predicated on the increasing volatility and unpredictability in energy landscapes, including fluctuating renewable outputs and changing load demands. To tackle these challenges, the AmEFC prototype incorporates a novel hierarchical control structure that leverages Renewable Energy Sources (RES), such as photovoltaic systems, wind turbines, and hydro pumps, alongside a sophisticated Battery Management System (BMS). Its prime objective is to maintain an uninterrupted power supply to critical loads, efficiently balance energy surplus through hydraulic storage, and ensure robust interaction with the main grid. A comprehensive Simulink model is developed to validate the functionality of the AmEFC, simulating real-world conditions and dynamic interactions among the components. The model assesses the system’s reliability in consistently powering critical loads and its efficacy in managing surplus energy. The inclusion of advanced predictive algorithms enables the AmEFC to anticipate energy production and consumption trends, integrating weather forecasting and inter-controller communication to optimize energy flow within and across micro-grids. This study’s significance lies in its potential to facilitate the seamless incorporation of RES into existing power systems, thus propelling the energy sector towards a more sustainable, autonomous, and resilient future. The results underscore the potential of such a system to revolutionize energy management practices and highlight the importance of smart controller systems in the era of smart grids.

A mass balanced model of trophic structure and energy flows of a semi-closed marine ecosystem

The marine ecosystem of the Jiaozhou Bay has degraded significantly in fisheries productivity and its ecological roles as spawning and nursery ground for many species of commercial importance has been declining in recent years. A mass-balanced trophic model was developed using Ecopath with Ecosim to evaluate the trophic structure of the Jiaozhou Bay for improving ecosystem management. The model were parameterized based on the fisheries survey data in the Jiaozhou Bay in 2011, including 23 species groups and one detritus group according to their ecological roles. The trophic levels of these ecological groups ranged from 1（primary producers and detritus） to4.3（large demersal fishes）. The estimated total system throughput was 12 917.10 t/（km^2·a）, with 74.59% and25.41% contribution of the total energy flows from phytoplankton and detritus, respectively. Network analyses showed that the overall transfer efficiency of the ecosystem was 14.4%, and the mean transfer efficiency was 14.5%for grazing food chain and 13.9% for detritus food chain. The system omnivory index（SOI）, Finn＇s cycled index（FCI） and connectance index（CI） were relatively low in this area while the total primary production/total respiration（TPP/TR） was high, indicating an immature and unstable status of the Jiaozhou Bay ecosystem. Mixed trophic impact analysis revealed that the cultured shellfish had substantial negative impacts on most functional groups. This study contributed to ecosystem-level evaluation and management planning of the Jiaozhou Bay ecosystem.

Ecosystem Productivity and Energy Flow of Three-Hardwood Forest

The main characteristics of energy environment, energy products, primary productivity and basic process ofenergy flow for three-hardwood forest(Juglans mandshurica, Fraxinus mandshurica, and Phellodendron amurense) werestudied. The research was mainly hased on the thcory and method of community energetics, dealing with fixed position,quantitative test and expcrimental analysis. The time-space dynamics of sun-radiation in three-hardwood forest were measured and the energy compartment model was set up. his rescarch work provided a scientitic basis for the exploitation, utilization and management of three-hardtwood forest.

Energy flow problem solution based on state estimation approaches and smart meter data

Accurate electric energy(EE)measurements and billing estimations in a power system necessitate the development of an energy flow distribution model.This paper summarizes the results of investigations on a new problem related to the determination of EE flow in a power system over time intervals ranging from minutes to years.The problem is referred to as the energy flow problem(EFP).Generally,the grid state and topology may fluctuate over time.An attempt to use instantaneous(not integral)power values obtained from telemetry to solve classical electrical engineering equations leads to significant modeling errors,particularly with topology changes.A promoted EFP model may be suitable in the presence of such topological and state changes.Herein,EE flows are determined using state estimation approaches based on direct EE measurement data in Watt-hours(Volt-ampere reactive-hours)provided by electricity meters.The EFP solution is essential for a broad set of applications,including meter data validation,zero unbalance EE billing,and nontechnical EE loss check.

Improved Soft Abrasive Flow Finishing Method Based on Turbulent Kinetic Energy Enhancing

Soft abrasive flow（SAF） finishing can process the irregular geometric surfaces, but with the matter of low processing efficiency. To address the issue, an improved SAF finishing method based on turbulent kinetic energy enhancing is proposed. A constrained flow passage with serration cross-section is constructed to increase the turbulence intensity. Taking the constrained flow passage as the objective, a two-phase fluid dynamic model is set up by using particle trajectory model and standard k-ε turbulence model, and the flow field characteristics of the flow passage are acquired. The numerical results show that the serration flow passage can enhance the turbulence intensity, uniform the particles distribution, and increase the particle concentration near the bottom wall. The observation results by particle image velocimetry（PIV） show that the internal vortex structures are formed in flow passage, and the abrasive flow takes on turbulence concentrating phenomenon in near-wall region. The finishing experiments prove that the proposed method can obtain better surface uniformity, and the processing efficiency can be improved more 35%. This research provides an abrasive flow modeling method to reveal the particle motion regulars, and canoffer references to the technical optimization of fluid-based precision processing.

Super energy flows in a waveguide filled with air and left-handed materials

The phenomena of super energy flows are studied theoretically and numerically in a parallel-plate waveguide which is filled with two layered equally-thick different media, i.e. air and specific left-handed materials （LHM） with εr1 = -1/（1 ＋δ） ＋iγ and μr1 = -（1 ＋ δ） ＋ iγ. In this special waveguide, two-directional super-energy flows are excited by a three-dimensional horizontal electric dipole at the same time, which has transmission patterns different from those of two-dimensional source and three-dimensional vertical electric dipole. We also show that the retardation and loss in LHM are sensitive to the amplitude of super power densities, and the dimensions of waveguide determine the propagating modes, which makes super energy flows more practical.

Renewable and Nonrenewable Energy Flow Resiliency for Day-to-Day Production and Consumption

Energy resilience is about ensuring a business and end-use consumers have a reliable,regular supply of energy and contingency measures in place in the event of a power failure,generating a source of power such as electricity for daily needs from an uninterrupted source of energy no matter either renewable or nonrenewable.Causes of resilience issues include power surges,weather,natural disasters,or man-made accidents,and even equipment failure.The human operational error can also be an issue for grid-power supply to go down and should be factored into resilience planning.As the energy landscape undergoes a radical transformation,from a world of large,centralized coal plants to a decentralized energy world made up of small-scale gas-fired production and renewables,the stability of electricity supply will begin to affect energy pricing.Businesses must plan for this change.The challenges that the growth of renewables brings to the grid in terms of intermittency mean that transmission and distribution costs consume an increasing proportion of bills.With progress in the technology of AI(Artificial Intelligence)integration of such progressive technology in recent decades,we are improving our resiliency of energy flow,so we prevent any unexpected interruption of this flow.Ensuring your business is energy resilient helps insulate against price increases or fluctuations in supply,becoming critical to maintaining operations and reducing commercial risk.In the form short TM(Technical Memorandum),this paper covers this issue.

Quasi-40-Day Oscillation and Its Teleconnection Structure together with the Possible Dependence on Conversion of Barotropic Unstable Energy of Temporal Mean Flow

A study is made of the distribution of the diagnostic quantity vector E and the teleconnection structure of 30-50 (quasi-40) day oscillation, together with the dependence on the conversion of barotropic unstable energy of mean flow in terms of ECWMF daily 500 hPa grid data in winter, indicating that the energy transportation is closely associated with the westerly jet position, with zonal (meridional) propagation in the strong (weak) wind region, that considerable conversion of barotropic energy occurs at the jet exit region where low-frequency oscillation gains energy from the mean flow, leading to maximum kinetic energy for the oscillation observed there, which is marked by evident barotropy in striking contrast to the baroclinicity at low latitudes and that the teleconnection core is related to the center of action in the atmosphere and bound up with the pattern of the west wind.

Dual-radiation-chamber coordinated overall energy efficiency scheduling solution for ethylene cracking process regarding multi-parameter setting and multi-flow allocation

Ethylene cracking process is the core production process in ethylene industry,and is paid more attention to reduce high energy consumption.Because of the interdependent relationships between multi-flow allocation and multi-parameter setting in cracking process,it is difficult to find the overall energy efficiency scheduling for the purpose of saving energy.The traditional scheduling solutions with optimal economic benefit are not applicable for energy efficiency scheduling issue due to the neglecting of recycle and lost energy,as well as critical operation parameters as coil outlet pressure(COP)and dilution ratio.In addition,the scheduling solutions mostly regard each cracking furnace as an elementary unit,regardless of the coordinated operation of internal dual radiation chambers(DRC).Therefore,to improve energy utilization and production operation,a novel energy efficiency scheduling solution for ethylene cracking process is proposed in this paper.Specifically,steam heat recycle and exhaust heat loss are considered in cracking process based on 6 types of extreme learning machine(ELM)based cracking models incorporating DRC operation and three operation parameters as coil outlet temperature(COT),COP,and dilution ratio according to semi-mechanism analysis.Then to provide long-term decision-making basis for energy efficiency scheduling,overall energy efficiency indexes,including overall output per unit net energy input(OONE),output-input ratio per unit net energy input(ORNE),exhaust gas heat loss ratio(EGHL),are designed based on input-output analysis in terms of material and energy flows.Finally,a multiobjective evolutionary algorithm based on decomposition(MOEA/D)is employed to solve the formulated multi-objective mixed-integer nonlinear programming(MOMINLP)model.The validities of the proposed scheduling solution are illustrated through a case study.The scheduling results demonstrate that an optimal balance between multi-flow allocation,multi-parameter setting,and DRC coordinated operation is reached,which achieves 3.37%and 2.63%decreases in net energy input for same product output and conversion ratio,as well as the 1.56%decrease in energy loss ratio.

Over-limit risk assessment method of integrated energy system considering source-load correlation

In an integrated energy system,source-load multiple uncertainties and correlations lead to an over-limit risk in operating state,including voltage,temperature,and pressure over-limit.Therefore,efficient probabilistic energy flow calculation methods and risk assessment theories applicable to integrated energy systems are crucial.This study proposed a probabilistic energy flow calculation method based on polynomial chaos expansion for an electric-heat-gas integrated energy system.The method accurately and efficiently calculated the over-limit probability of the system state variables,considering the coupling conditions of electricity,heat,and gas,as well as uncertainties and correlations in renewable energy unit outputs and multiple types of loads.To further evaluate and quantify the impact of uncertainty factors on the over-limit risk,a global sensitivity analysis method for the integrated energy system based on the analysis of covariance theory is proposed.This method considered the source-load correlation and aimed to identify the key uncertainty factors that influence stable operation.Simulation results demonstrated that the proposed method achieved accuracy to that of the Monte Carlo method while significantly reducing calculation time.It effectively quantified the over-limit risk under the presence of multiple source-load uncertainties.

Generalised energy conservation law for chaotic phenomena

Chaotic phenomena are increasingly being observed in all fields of nature,where investigations reveal that a natural phe nomenon exhibits nonlinearities and attempts to reveal their deep underlying mechanisms.Chaos is normally understood as“a state of disorder”,for which there is as yet no universally accepted mathematical definition.A commonly used concept states that,for a dynamical system to be classified as chaotic,it must have the following properties:be sensitive to initial conditions,show topological transitivity,have densely periodical orbits etc.Revealing the rules that govern chaotic motion is thus an important unsolved task for exploring nature.W e present herein a generalised energy conservation law governing chaotic phenomena.Based on two scalar variables,viz.generalised potential and kinetic energies defined in the phase space describing nonlinear dynamical systems,we find that chaotic motion is periodic motion with infinite time period whose time-averaged generalised potential and kinetic energies are conserved over its time period.This implies that,as the averaging time is increased,the time-averaged generalised potential and kinetic energies tend to constants while the time-averaged energy flows,i.e.,their rates of change with time,tend to zero.Numerical simulations on reported chaotic motions,such as the forced van der Pol system,forced Duffing system,forced smooth and discontinuous oscillator,Lorenz’s system,and Rossler's system,show the above conclusions to be correct according to the results presented herein.This discovery may indicate that chaotic phenomena in nature could be controlled because,even though their instantaneous states are disordered,their long-time averages can be predicted.

Numerical Investigation of Flow Motion and Performance of A Horizontal Axis Tidal Turbine Subjected to A Steady Current

Horizontal axis tidal turbines have attracted more and more attentions nowadays, because of their convenience and low expense in construction and high efficiency in extracting tidal energy. The present study numerically investigates the flow motion and performance of a horizontal axis tidal turbine with a supporting vertical cylinder under steady current. In the numerical model, the continuous equation and incompressible Reynolds-averaged Navier-Stokes equations are solved, and the volume of fluid method is employed to track free surface motion. The RNG k-ε model is adopted to calculate turbulence transport while the fractional area/volume obstacle representation method is used to describe turbine characteristics and movement. The effects of installation elevation of tidal turbine and inlet velocity on the water elevation, and current velocity, rotating speed and resultant force on turbine are discussed. Based on the comparison of the numerical results, a better understanding of flow structure around horizontal axis tidal turbine and turbine performance is achieved.

Kinetic and Available Potential Energy Transport during the Stratospheric Sudden Warming in January 2009

The local features of transient kinetic energy and available potential energy were investigated using ECMWF （European Centre for Medium-Range Weather Forecasts） Interim Reanalysis data for the stratospheric sudden warming （SSW） event of January 2009. The Western Europe high plays important roles in the propagation of the energy from North America to Eurasian. When the Western Europe high appeared and shifted eastward, energy conversions increased and energy propagated from North America to Eurasian as a form of interaction energy flow. The baroclinic conversion between transient-eddy kinetic energy （Ke） and transient-eddy available potential energy （Ae） and the horizontal advection of geopotential height were approximately one order of magnitude less than Ke and Ae generation terms. So, these terms were less important to this SSW event.

Global Electricity Demand and Clean Energy Source Growth Scenario

It is impossible to overstate the importance of energy.Just thinking about where humanity would be without it may be enough to demonstrate this point.Like in the past,energy will play a vital role in shaping future industries,cities,nations,and the world.That is why we believe that energy is a critical factor in shaping future paradigms in any target entity or world.To have a better understanding of the role that energy plays in the world today and in the future,in this article,we briefly look at the definition of energy and its different forms,and review some data related to energy consumption in the world and the United States.Furthermore,as a source of clean energy,we believe the future of nuclear power technology,despite the challenges it faces,is an important option for this country and the rest of the world to meet future energy needs without emitting CO(carbon monoxide)and CO2(carbon dioxide),or other GHGs(greenhouse gases),and other atmospheric pollutants and it is more efficient among its other comparable sources of renewable energies,such as solar,wind,etc.Globally,renewables made up 29 percent of electricity generation in 2020,much of it from hydro-power(16.8 percent).A record amount of over 256 GW of renewable power capacity was added globally during 2020 and continues to be the focal point for climate and energy solutions.Demand for electricity is direct function of population growth globally and is also driven by the present century’s extraordinary technological developments.

Energy Driven by Internet of Things Analytics and Artificial Intelligence

Developing an integrated and intelligent approach to securing the ITE(information technology environment)is an emergent and evolving concern for every organization and consumer entity during the last few decades.Major topics of concern include“SI”(security intelligence),“D-DA”(data-driven analytics),“PE”(proven expertise),and“R-TD”(real-time defense)capabilities.“DRBTs”(dynamic response behavior types)include“incident response”,“endpoint management”,“threat intelligence”,“network security”,and“fraud protection”.The consumer demand for electricity as essential public access and service is indexed to population growth estimates.Consumer-driven economies continue to add electrical consumption to their grids even though improvements in lower-power consumption and higher design efficiencies are present in new electric-powered products.Dependence on the production of electrical energy has no peer replacement technology and creates a societal vulnerability to targeted public electrical grid interruptions.When access to,or production of,electrical power is interrupted,the result is a“Mass Effect”every consumer feels with equal distribution.Electric grid security falls directly into the levels of authorized,and unauthorized,access via the“IoT”(Internet of Things)concepts,and the“IoM2M”(Internet of Machine-to-Machine)integration.Electrical grid operations that include production and network management augment each other in order to support the demand for electricity every day either in peak or off-peak,thus cybersecurity plays a big role in the protection of such assets at our disposal.With help from AI(artificial intelligence)integrated into the IoT a resilient system can be built to protect the electric grid system nationwide and will be able to detect and preempt Smart Malware attacks.

Effects of relaxation time on start-up time for starting flow of Maxwell fluid in a pipe

Although the analytical solution of the starting flow of Maxwell fluid in a pipe has been derived for a long time, the effect of relaxation time λ on start-up time ts of this flow is still not well understood. Especially, there exist a series of jumps on the ts-λ. curve. In this paper we introduce a normalized mechanical energy by mode decomposition and mathematical analogy to describe the start-up process. An improved definition of start-up time is presented based on the normalized mechanical energy. It is proved that the ts-λ. curve contains a series of jumps if λ is larger than a critical value. The exact positions of the jumps are determined and the physical reason of the jumps is discussed.

Momentum-Dependent Symmetry Potential from Nuclear Collective Flows in Heavy Ion Collisions at Intermediate Energies

Within the isospin-dependent quantum molecular dynamics model, we investigate the nuclear collective flows produced in semi-central 197 Au＋197 Au collisions at intermediate energies. The neutron proton differential flows and difference of neutron proton collective flows are sensitive to the momentum-dependent symmetry potential. This sensitivity is less affected by both the isoscalar part of nuclear equation of state and in-medium nucleon- nucleon cross sections. Moreover, this sensitivity becomes pronounced with increasing the rapidity cut.

An Uncertain Optimal Energy Flow Model for CCHP Campus Microgrid Using Parameterized Probability Boxes

Due to the uncertain fluctuations of renewable energy and load power, the state variables such as bus voltages and pipeline mass flows in the combined cooling, heating, and power campus microgrid(CCHP-CMG) may exceed the secure operation limits. In this paper, an optimal energy flow(OEF) model for a CCHP-CMG using parameterized probability boxes(p-boxes) is proposed to describe the higher-order uncertainty of renewables and loads. In the model, chance constraints are used to describe the secure operation limits of the state variable p-boxes, and variance constraints are introduced to reduce their random fluctuation ranges. To solve this model, the chance and variance constraints are transformed into the constraints of interval cumulants(ICs) of state variables based on the p-efficient point theory and interval Cornish-Fisher expansion. With the relationship between the ICs of state variables and node power, and using the affine interval arithmetic method, the original optimization model is finally transformed into a deterministic nonlinear programming model. It can be solved by the CONOPT solver in GAMS software to obtain the optimal operation point of a CCHP-CMG that satisfies the secure operation requirements considering the higher-order uncertainty of renewables and loads. Case study on a CCHP-CMG demonstrates the correctness and effectiveness of the proposed OEF model.

A network-based virtual slack bus model for energy conversion units in dynamic energy flow analysis

Integrated energy system(IES) is a viable route to “carbon peak and carbon neutral”. As the basis and cornerstone of economic operation and security of IES, energy flow calculation(EFC) has been widely studied. Traditional EFC focuses on the single or distributed slack bus models, which results in the lack of unlimited power to maintain system operation, especially for electric power grid working in islanded or coupled mode. To deal with this problem, this paper proposes a network-based virtual-slack bus(VSB) model in EFC. Firstly, considering the anticipated growth of energy conversion units(ECUs) with power adjustment capacity, the generators and ECUs are together modeled as a virtual slack bus model to reduce the concentrated power burden of IES. Based on this model, a power sensitivity method is designed to achieve the power sharing among the ECUs, where the power can be allocated adaptively based on the network conditions. Moreover, the method is helpful to maintain the voltage and pressure profile of IES. With these changes, a dynamic energy flow analysis including virtual slack bus types is extended for IES.It can realize the assessment of the system state. Finally, simulation studies illustrate the beneficial roles of the VSB model.

Optimal Energy Flow and Nodal Energy Pricing in Carbon Emission-embedded Integrated Energy Systems

Green energy is driving the evolutions of energy industry and carbon emission is becoming an important concern.Considering the increasing couplings among various energy sectors,this paper investigates multi-period optimal energy flow and energy pricing in carbon-emission embedded integrated energy systems,including electricity,natural gas,and district heating networks.Firstly,an optimal scheduling model of integrated energy systems was proposed in this paper.The models of DC power flow,natural gas pipeline flow and heating network energy flow are presented and linearized for the optimization problem.Natural gas-fired generators and combined heat and power(CHP)units are modeled as coupling components of electricitygas and electricity-heating networks.Then,based on the optimal scheduling model,the locational marginal prices(LMP)for electricity,natural gas and heating network are determined.Moreover,the carbon emission caused by energy production has been taken into account in the optimal scheduling and energy pricing process.Finally,case studies on a combined network consisting of IEEE 39-bus system,Belgium 20-node natural gas system and 6-node heating system demonstrate the effectiveness of the proposed model and the impacts of carbon emission on system scheduling and LMP.