Energy Fluctuations Research Articles

Brownian fluctuations of kinetic energy under Lorentz force

In stochastic thermodynamics, significant attention has been given to studying the statistical behavior of thermodynamic quantities, such as heat and work. However, fluctuations in other quantities, such as kinetic energy and internal energy, can also exhibit intriguing statistical properties. In this study, we investigate the fluctuations of kinetic energy within an initially equilibrated underdamped Brownian particle subsequently exposed to a Lorentz force, comprising both electric and magnetic fields, and provide insights through the examination of the characteristic function, central moments, and kinetic energy distribution.

Entropy-ruled nonequilibrium charge transport in thiazolothiazole-based molecular crystals: a quantum chemical study.

The charge and energy fluctuations in molecular solids are crucial factors for a better understanding of charge transport (CT) in organic semiconductors. The energetic disorder-coupled molecular charge transport is still not well-established. Moreover, the conventional Einstein's diffusion (D)-mobility (μ) relation fails to explain the quantum features of organic semiconductors, including nonequilibrium and degenerate transport systems, where kB is the Boltzmann constant, T is the temperature and q is the electric charge. To overcome this issue, a unified version of the entropy-ruled D/μ relation was proposed by Navamani (J. Phys. Chem. Lett., 2024, 15, 2519-2528) for hopping and band transport systems as where d, η and heff are the dimension (d = 1, 2, 3), chemical potential and effective entropy, respectively. Within this context, we investigate the CT properties of 2,5-bis(4-methoxyphenyl)thiazolo[5,4-d]thiazole (MOP-TZTZ) and 2,5-bis(2,4,5 trifluorophenyl)-thiazolo[5,4-d]thiazole (TFP-TZTZ) molecular solids using electronic structure calculations and the entropy-ruled method. The CT key parameters such as charge transfer integral and site energy are computed by matrix elements of the Kohn-Sham Hamiltonian. Using Marcus theory, the charge transfer rate is numerically calculated for MOP-TZTZ and TFP-TZTZ molecular crystals under different site energy disorder (ΔEij(E⃑)) situations. Using our entropy-ruled method, the exact diffusion-mobility (D/μ) and other transport quantities such as thermodynamic density of states, conductivity, and current density are calculated for these derivatives at different applied electric field values via the site energy disorder. The theoretical results show that the molecule TFP-TZTZ has good hole mobility (∼0.012 cm2 V-1 s-1) at a site energy disorder value of 90 meV. The obtained ideality factor from the Navamani-Shockley diode current density equation categorizes the typical transport as either the Langevin-type or Shockley-Read-Hall mechanism in the studied molecular solids. Our analysis clearly shows that both the electron and hole transport in these MOP-TZTZ and TFP-TZTZ molecules follow the trap-free Langevin mechanism, which is indeed ideal for designing charge-transporting molecular devices.

Enhancing the ability of single-pixel imaging against the source’s energy fluctuation by complementary detection

Enhancing the ability of single-pixel imaging against the source’s energy fluctuation by complementary detection

Bidirectional Long Short-Term Memory (Bi-LSTM) Hourly Energy Forecasting

The growing demand for energy, especially in urban and densely populated areas, has driven the need for smarter and more efficient approaches to energy resource management. One of the main challenges in energy management is fluctuations in energy demand and production. To overcome this challenge, accurate and careful forecasting of hourly energy fluctuations is required. One method that has proven effective in time series forecasting is using deep learning. The research phase uses the CRISP-DM data mining methodology as a common problem solver for business and research. The scenarios tested in the study used 5 attribute selection scenarios based on correlation values based on target attributes and 2 normalization scenarios. Then, the deep learning model used is Bi-LSTM with hyperparameter tuning grid search. Performance measurement evaluation is performed with MAPE, RMSE, and R2. Based on the tests conducted, it was found that the Bi-LSTM model produced the best MAPE of 7.7256%. RMSE of 0.1234. and R2 of 0.6151 at min-max normalization. In comparison, the results on the z-score normalization are lower with the best MAPE value produced at 10.5525%. RMSE of 0.7627. and R2 of 0.4186.

Optimizing PV integration: Addressing energy fluctuations through BIPV and rooftop PV synergy

The widespread availability and affordability of photovoltaic (PV) systems are driving the future of demand-side generation towards end-user-based PV plants. Building-integrated PV systems offer an additional source of electrical energy, but their power output depends on external factors like solar insolation, weather conditions, geographical location, and earth’s rotation, causing non-constant energy generation, even in ideal weather conditions. In grid-connected systems, this variability leads to fluctuations in grid demand. Both BIVP and rooftop PV systems are photovoltaic-based, but their installation differences result in distinct energy generation characteristics. To address this, we propose an innovative approach to optimally integrate BIPV and rooftop PV systems by leveraging their contradictory energy generation nature. By employing mathematical and evolutionary algorithms to design an optimal system and develop a multi-objective optimization model, we address practical design issues. The outcome of these single and multi-objective systems helps minimize fluctuations in grid dependency throughout the year. The proposed system is validated on the IEEE-33 bus radial distribution network using the B&R X20CP1586 PLC, confirming its effectiveness in ensuring a stable and reliable grid performance while mitigating energy fluctuation impacts.

On the Role of Alfvénic Fluctuations as Mediators of Coherence within Interplanetary Coronal Mass Ejections: Investigation of Multi-spacecraft Measurements at 1 au

Interplanetary coronal mass ejections (ICMEs) are defined as “coherent” if they are capable of responding to external perturbations in a collective manner. This implies that information must be able to propagate across ICME structures, and if this is not the case, single-point in situ measurements cannot be considered as indicative of global ICME properties. Here, we investigate the role of Alfvénic fluctuations (AFs) as mediators of ICME coherence. We consider multipoint magnetic field and plasma measurements of 10 ICMEs observed by the ACE and Wind spacecraft at 1 au at longitudinal separations of 0.5°–0.7°. For each event, we analyze the Alfvénicity in terms of the residual energy and cross helicity of fluctuations, and the coherence in terms of the magnetic correlation between Wind and ACE. We find that ∼65% and 90% of ICME sheaths and magnetic ejecta (MEs), respectively, present extended AFs covering at least 20% of the structure. Cross helicity suggests AFs of solar and interplanetary origin may coexist in the ICME population at 1 au. AFs are mainly concentrated downstream of shocks and in the back of MEs. The magnetic field is poorly correlated within sheaths, while the correlation decreases from the front to the back of the MEs for most magnetic field components. AFs are also associated with lower magnetic field correlations. This suggests either that ICME coherence is not mediated by Alfvén waves, implying that the coherence scale may be smaller than previously predicted, or that the magnetic field correlation is not a measure of coherence.

Real-time bremsstrahlung detector as a monitoring tool for laser–plasma proton acceleration

Abstract Real-time evaluation of laser-driven byproducts is crucial for state-of-the-art facilities operating at high repetition rates. This work presents real-time measurements of hard X-rays (bremsstrahlung radiation) generated from the interaction of high-intensity laser pulses with solid targets in the target normal sheath acceleration regime using a scintillator stack detector. The detector offers insights into the effectiveness of laser–plasma interaction through measured fluctuations in bremsstrahlung radiation temperature and scintillation light yield on a shot-to-shot basis. Moreover, a strong correlation of the bremsstrahlung measurements (i.e., temperature and yield) with the cutoff energy of laser-driven protons was observed. The scintillator stack detector serves not only as a diagnostic for online monitoring of the laser–plasma interaction but also as a promising tool for estimating proton energy fluctuations in a non-disruptive manner, which is particularly important when direct proton source characterization is impractical, for example, during experiments aimed at irradiating user samples with the accelerated proton beam.

Energy stability in multi-timescale pump-probe spectroscopy with free-running lasers (ADASOPS)

ADASOPS pump-probe spectroscopy is a multi-timescale technique that is spreading rapidly especially in the field of biomolecule dynamics. Based on slight variations of the laser repetition rate, it is simple to implement and can cover a time range extending from a hundred femtoseconds to a millisecond or more. We have studied the energy fluctuations associated with this approach and have proposed a method for overcoming any instabilities.

Elucidating ultranarrow 2F7/2 to 2F5/2 absorption in ytterbium(iii) complexes.

Achieving ultranarrow absorption linewidths in the condensed phase enables optical state preparation of specific non-thermal states, a prerequisite for quantum-enabled technologies. The 4f orbitals of lanthanide(iii) complexes are often referred to as "atom-like," reflecting their isolated nature, and are promising substrates for the optical preparation of specific quantum states. To better understand the photophysical properties of 4f states and assess their potential for quantum applications, theoretical building blocks are required for rapid screening. In this study, an atomic-level perturbative calculation (i.e., spin-orbit crystal field, SOCF) is applied to various Yb(iii) complexes to investigate their linear absorption and emission through a fitting mechanism of their experimentally determined transition energies and oscillator strengths. In particular, the optical properties of (thiolfan)YbCl(THF) (thiolfan = 1,1'-bis(2,4-di-tert-butyl-6-thiomethylenephenoxy)ferrocene), a recently reported complex with an ultranarrow optical linewidth, are computed and compared to those of other Yb(iii) compounds. Through a transition energy sampling study, major contributors to the optical linewidth are identified. We observe particularly isolated f-f transitions and narrow linewidths, which we attribute to two distinct factors. Firstly, the ultra-high atomic similarity of the orbitals involved in the optical transition, along with the presence of an anisotropic crystal field, collectively contribute to the observed narrow transitions. Secondly, we note highly correlated excited-ground energy fluctuations that serve to greatly suppress inhomogeneous line-broadening. This article illustrates how SOCF can be used as a low-cost method to probe the influence of crystal field environment on the optical properties of Yb(iii) complexes to assist the development of novel lanthanide series quantum materials.

Artificial neural network modeling of solar photovoltaic panel energy output

Solar panel energy output is an essential parameter for the design and operation of renewable energy systems. Previously, little was known about the precise relationship between the energy outputs of solar panels with various meteorological, radiometric, and weather conditions in the southern California region. Without precise modeling or prediction systems, solar energy can potentially be wasted due to grid energy fluctuation. Thus, it is intended to use an artificial neural network (ANN) to develop solar panel energy output prediction model with a high degree of accuracy. A self-developed feedforward ANN model utilizing the Rectified linear unit (ReLu) activation function was used in the present study. Meteorological, weather, and sun irradiation data collected throughout the last year from a residential location have been used to train the models. The model’s performance was identified based on the minimum mean absolute error (MAE) and root mean square error (RMSE) and maximum linear correlation coefficient (R2). Further, the present self-developed ANN model was consistent with other solar energy experimental results and theoretical analysis. The developed ANN model using the Python programming language achieved a high R2 of more than 85% which ascertains the accuracy and suitability of the model to predict the daily solar energy output in local southern California area. This ANN modeling approach can be extended to many other applications such as SCORE, commercial, and residential building design.

Low carbon scheduling method of electric power system considering energy-intensive load regulation of electrofused magnesium and wind powerfluctuation stabilization

The significant expansion of wind power has presented challenging obstacles to power system operation. The inherent stochasticity and variability of wind power have emerged as critical issues that impede the advancement of wind energy, affecting its integration and volatility control. Moreover, with the momentum of carbon peaking and carbon neutrality objectives, reducing CO2 emissions has become urgent. This study proposes a low-carbon dispatching methodology for power systems to address these challenges. It considers regulating energy-intensive loads of electrically fused magnesium and mitigating wind power fluctuations. The approach involves integrating electrically Fused magnesium loads with tight regulation characteristics on the demand side alongside thermal power plants to optimize the power grid and dispatch collectively. And battery energy storage devices are introduced into various wind power stations on the supply side to smooth out wind power fluctuations. The objective function aims to minimize the sum of the average variance of power fluctuations at all wind power busbars while adhering to constraints of maximum wind power consumption and minimum carbon emissions from thermal power plants. The problem is solved using genetic algorithms. Ultimately, we show how effective our proposed low-carbon dispatching method for power systems is, which deals with regulating energy-intensive loads of electrically fused magnesium and reducing wind power fluctuations. We demonstrate this through a case study of wind curtailment, CO2 emissions, wind power fluctuations, and other related parameters under different conditions. The approach can enhance renewable energy consumption, reduce carbon emissions, and mitigate the power fluctuations of renewable energy.

A data-driven model for power system operating costs based on different types of wind power fluctuations

The stochastic and intermittent features of wind power as well as the high percentage of wind power grid-connected significantly increase the additional operating costs of the power system. It is difficult to accurately calculate the impact of complex fluctuations in wind power on additional operating costs. To solve the above problems, a power system operating cost model adapted to various wind power fluctuation processes is established. Firstly, based on a two-layer clustering strategy, different types of wind power fluctuations are obtained. Then, a production simulation model of the power system with renewable energy is established. The production simulation model costs include thermal plant operating costs, energy storage system operating costs, positive reserve costs and negative reserve costs. With the optimization objective of minimizing the total operating cost of the power system, realistic and representative system operating parameters and cost samples are obtained for various wind power fluctuations and different wind power grid-connected scenarios. Finally, a data-driven approach based on a deep neural network algorithm is proposed to achieve precise mapping between wind energy fluctuations and the operating costs of power systems and thermal power units, and the operating costs of the power system during the four seasons with different types of wind power fluctuations can be precisely analyzed. The results demonstrate that the method proposed in this paper has high simulation accuracy for the overall simulation operating cost of the power system and the operating cost of thermal power plants. The simulation errors are 4%–18% and 3%–13%, respectively, which verified the effectiveness of the method.

Interlayer Phonon Coupling from Heavy and Light Sublayers in a Natural Van der Waals Superlattice.

Layered compounds characterized by van der Waals gaps are often associated with relatively weak interlayer particle interactions. However, in specific scenarios, these seemingly feeble forces can exert an impact on interlayer interactions through subtle energy fluctuations, which can give rise to a diverse range of physical and chemical properties, particularly intriguing in the context of thermal transport. In this study, taking a natural superlattice composed of alternately stacked PbS and SnS2 sublayers as a model, we proposed that in a superlattice, there is strong hybridization between acoustic phonons of heavy sublayers and optical phonons of light sublayers. We identified newly generated vibration modes in the superlattice, such as interlayer shear and breathing, which exhibit lower sound velocity and contribute less to heat transport compared to their parent materials, which significantly alters the thermal behaviors of the superlattice compared to its bulk counterparts. Our findings on the behavior of interlayer phonons in superlattices not only can shed light on developing functional materials with enhanced thermal dissipation capabilities but also contribute to the broader field of condensed matter physics, offering insights into various fields, including thermoelectrics and phononic devices, and may pave the way for technological advancements in these areas.

Molecular docking, molecular dynamics simulation, and MM/PBSA analysis of ginger phytocompounds as a potential inhibitor of AcrB for treating multidrug-resistant Klebsiella pneumoniae infections

The emergence of Multidrug resistance (MDR) in human pathogens has defected the existing antibiotics and compelled us to understand more about the basic science behind alternate anti-infective drug discovery. Soon, proteome analysis identified AcrB efflux pump protein as a promising drug target using plant-driven phytocompounds used in traditional medicine systems with lesser side effects. Thus, the present study aims to explore the novel, less toxic, and natural inhibitors of Klebsiella pneumoniae AcrB pump protein from 69 Zingiber officinale phyto-molecules available in the SpiceRx database through computational-biology approaches. AcrB protein’s homology-modelling was carried out to get a 3D structure. The multistep-docking (HTVS, SP, and XP) were employed to eliminate less-suitable compounds in each step based on the docking score. The chosen hit-compounds underwent induced-fit docking (IFD). Based on the XP GScore, the top three compounds, epicatechin (−10.78), 6-gingerol (−9.71), and quercetin (−9.09) kcal/mol, were selected for further calculation of binding free energy (MM/GBSA). Furthermore, the short-listed compounds were assessed for their drug-like properties based on in silico ADMET properties and Pa, Pi values. In addition, the molecular dynamics simulation (MDS) studies for 250 ns elucidated the binding mechanism of epicatechin, 6-gingerol, and quercetin to AcrB. From the dynamic binding free energy calculations using MM/PBSA, 6-gingerol exhibited a strong binding affinity towards AcrB. Further, the 6-gingerol complex’s energy fluctuation was observed from the free energy landscape. In conclusion, 6-gingerol has a promising inhibiting potential against the AcrB efflux pump and thus necessitates further validation through in vitro and in vivo experiments. Communicated by Ramaswamy H. Sarma

Durability of Water Electrolysis Cells Against the Long-Term Voltage Fluctuation Simulating Wind Power

Introduction Water electrolysis is an important technology for efficiently combining the renewable energy with hydrogen energy. For directly using the renewable energy to produce hydrogen and oxygen through water electrolysis, the long-term durability against the voltage fluctuation of the renewable energy is necessary. We have particularly focused on the voltage fluctuation of the wind power. Typical 24-h wind power voltage fluctuations were obtained from one of three wind lenses of a 10-kW Muti-Lens Turbine® installed in Hibikinada, Japan, and applied to a single PEM water electrolysis cell after reducing the actual voltage to one-hundredth of the original value. When 20-d voltage fluctuations were applied, no degradation of the water electrolysis cell was observed if 1-h rest time was included after every 48-h voltage fluctuations for removing stagnated gases.Therefore, we have recently designed two types of voltage fluctuation protocols with different upper potential (2.0 V and 2.3 V) based on actual wind voltage fluctuations and evaluated durability of water electrolysis cells equivalent to 160 days.1 In both cases, reversible and irreversible current losses were observed. Reversible performance degradation was found to be most likely due to the gas stagnation and the reversible change of the oxidation state of iridium oxide catalysts. On the other hand, a few percent of the irreversible loss was found, which was mostly due to physical changes of the anode catalyst layer.In this study, to understand the irreversible loss more deeply, further long-term durability was evaluated and deterioration mechanisms were investigated. Experimental Water electrolytic cells were prepared by spray printing 46.3% Pt/KB and IrO2 for the cathode and the anode, respectively, on a Nafion 117 membrane. Voltage fluctuations with an upper limit of 2.3 V, shown in Fig. 1, were applied to a water electrolysis cell after accelerating the voltage fluctuation five times. Repeating the voltage fluctuation 228 times was defined as one set, corresponding to 48 h of actual operation. A total of 160 sets, corresponding to 320 d of actual operation, were performed in order to evaluate the durability of water electrolysis cells. The potential was applied to the anode, while maintaining the cathode at ~0 V.Electrochemical measurements of AC impedance and I-V performance were done at the beginning and after each set. Each overvoltage was separately analyzed. CV measurements of the anode side were also performed at the beginning and after 160 sets.For material characterizations, FIB-SEM observation followed by three-dimensional reconstruction were performed to evaluate the anode catalyst layer. Result and discussion A similar trend of decreasing and recovering the current to our previous study1 was confirmed in the extended voltage fluctuations. On the other hand, the recovery speed of the current was found to be slowed down. This may be related to increase in hydrophilicity of the IrO2 catalyst surface, which was highly oxidized by cycling to 2.3 V. Owing to increased hydrophilicity, pores of the catalyst layer were mostly filled with water and then the limited space was available for gas transfer. Although the oxidation state reversibly changed, its speed was probably slow.Regarding to the irreversible current loss, it was more pronounced. Figure 2 shows a cross-sectional image of the anode catalyst layer after the test equivalent to 320 d. An increase in the thickness of the anode, a part of which peeled off, was confirmed. This is most likely due to stagnated gas has increased the pressure within the anode catalyst layer. Since the gas stagnation is related to the initial pore structure of the anode catalyst layer, three-dimensional reconstruction of the anode structure using hundreds of FIB-SEM images was performed and analyzed in detail while comparing to the structure in our previous study. As a result, the water electrolysis cell used in this study had a relatively dense structure, which might be related to the amount of Nafion ionomer added in the anode catalyst layer, probably led to more gas stagnation, leading to an irreversible change of the anode structure. Reference (1) Y. Honsho et. al., J Power Sources 564, 232826, 2023. Figure 1

Research on Wind Energy Fluctuation Stabilization and Hybrid Energy Storage Capacity Optimization Strategy Based on EWD

Research on Wind Energy Fluctuation Stabilization and Hybrid Energy Storage Capacity Optimization Strategy Based on EWD

Iridium Oxide Nanorods Supported on Sb-Doped SnO2 As Highly Active Oxygen Evolution Catalysts for PEM Water Electrolysis

With the increasing installation of renewable energy generation systems such as solar and wind power, efficient energy storage devices are required to overcome the problem of fluctuations in the availability of electric energy. In particular, the splitting of water by electrolysis to produce storable hydrogen and oxygen appears to be one of the most promising approaches for large-scale energy storage. Proton exchange membrane water electrolysis (PEMWE) has received a great deal of attention due to its ability to respond quickly to renewable energy fluctuations and to operate at high current densities. However, a key limitation of PEMWE is the high cost associated with the use of high loadings of noble metal Ir catalysts at the anode electrode, which is necessary due to the slow kinetics of the oxygen evolution reaction (OER). Therefore, reducing the Ir loading at the OER electrode is essential to realize large-scale applications of PEMWE. This requires the development of highly active OER catalysts to accelerate the reaction or reduce the overpotential. Herein, we report a remarkably active OER catalyst of Ir oxide with nanorod-like structure supported on antimony-doped tin oxide (Sb-SnO2), showing an order of magnitude increase in Ir mass-specific activity than a commercial IrOx catalyst at 1.5 V vs. RHE. We also investigated the temperature dependence of the OER activity, as a way to elucidate the mechanism of enhanced catalysis, from both experimental and theoretical perspectives.The Sb-SnO2 support material (Sb dopant content 4 at.%) with fused-aggregate network structure was prepared by flame pyrolysis method.1 The as-prepared support powder was annealed at 700 oC for 2 h in air using a rotary kiln furnace. Sb-SnO2 supported IrOx nanorods (NRs) catalyst was synthesized by a colloidal method.2 The obtained IrOx NRs/Sb-SnO2 powder was heat-treated at 350 °C in N2 atmosphere for 2 h and at 150 °C in 1% H2 (N2 balance) for 2 h. The Ir loading was determined to be 49.3 wt.% by inductively coupled plasma optical emission spectrometry (ICP-OES). The OER activity was evaluated in 0.1 M HClO4 at 25 to 80 oC using the rotating disk electrode (RDE) technique.2,3 From Figure 1a, it can be seen that the Sb-SnO2 support particles present an interconnected network structure, which is considered to be beneficial to enhance the electron conduction and gas diffusion in the electrocatalytic reaction. On the support, the majority of IrOx nanoparticles are shown to have coalesced, forming rodlike structures. Figure 1b shows OER polarization curves (iR-corrected and normalized to the Ir mass) of the IrOx NRs/Sb-SnO2 catalyst compared to commercial IrOx catalyst (TANAKA Precious Metals). It is clear that the IrOx NRs/Sb-SnO2 catalyst significantly outperforms the IrOx reference catalyst over the entire potential range, and the OER onset potential of the IrOx NRs/Sb-SnO2 catalyst is lower. At 1.5 V (Figure 1c), the IrOx NRs/Sb-SnO2 catalyst exhibits a mass activity (MA) of 0.40 A mg-1 Ir, which is about 10 times higher than that of the IrOx reference catalyst. This dramatically enhanced activity makes it possible to significantly reduce the Ir loading on the anode of PEMWE. Experimental observations and density functional theory (DFT) calculations suggest that the nanorod geometry and strong metal-support interactions may play an important role in lowering the energy barrier for the crucial first step of the OER, thereby enhancing the OER activity. Acknowledgement This work was partly supported by the JSPS KAKENHI (23H02059) and by the project from the New Energy and Industrial Technology Development Organization (NEDO) of Japan.

Representation of three-dimensional mass distribution of the Earth's interior by biorthogonal series and its use for studying internal structure of the planet

This paper utilizes the mathematical concept of approximation within an ellipsoid from a single viewpoint to present the spatial mass distribution function of the Earth's interior and its internal potential. The primary focus lies in constructing the volume distribution of masses in the planet's interior, with the expansion coefficients being linear combinations of the Stokes constants. Several possible approaches are suggested for determining accurately these coefficients employing three-dimensional (biorthogonal) polynomials. By expressing the mass distribution function of the Earth's interior and its internal potential as a series, an algorithm is introduced for the calculation of gravitational energy. It allows us to estimate fluctuations in gravitational energy. The implementation of this algorithm offers the means of establishing the extent to which the Earth deviates from a state of hydrostatic equilibrium as a celestial body. Due to the aforementioned method, calculations have been conducted to validate its effectiveness and reliability. This example is given as an illustration of a given method for studying the internal structure of planets.

The Value of Energy Storage in Facilitating Renewables: A Northeast Area Analysis

The cross-regional and large-scale transmission of new energy power is an inevitable requirement to address the counter-distributed characteristics of wind and solar resources and load centers, as well as to achieve carbon neutrality. However, the inherent stochastic, intermittent, and fluctuating nature of wind and solar power poses challenges for the stable bundled dispatch of new energy. Leveraging the regulation flexibility of energy storage offers a potential solution to mitigate new energy fluctuations, enhance the flexibility of the hybrid energy systems, and promote bundled dispatch of new energy for external transmission. This paper takes energy storage as an example and proposes a capacity configuration optimization method for a hybrid energy system. The system is composed of wind power, solar power, and energy storage, denoted by the wind–solar–energy storage hybrid energy systems. The objective is to quantify the support provided by energy storage to bundled dispatch of new energy, namely determining the new energy transmission capacity that can be sustained per unit of energy storage. The results demonstrate that the proposed method effectively improves the bundled dispatch capacity of new energy. Moreover, the obtained configuration results can be tailored based on different wind–solar ratios, allowable fluctuation rates, and transmission channel capacities, rendering the approach highly valuable for engineering practicality.

Machine Learning-Based Optimization of Wind-PV Solution for Grid Demand

This research addresses the imperative need for innovative approaches to harness renewable resources while ensuring grid stability amidst global electricity demand. The proposed optimization methodology, centered on machine learning, harmonizes the intermittent output of Wind-PV systems with dynamic grid requirements. Leveraging historical meteorological data, energy trends, and real-time demand, the study employs ensemble learning, specifically the CNN (Convolutional Neural Network) algorithm, for accurate forecasting of PMSG-based (Permanent Magnet Synchronous Generator) Wind-PV system output. The two-tiered optimization approach involves short-term prediction using the CNN model and real-time correction through a genetic algorithm, enhancing the Capacity Utilization factor from 73% to 92% and reducing mean absolute error from 8% to 4.5%. This method not only anticipates energy fluctuations efficiently but also contributes to grid stability and decreased reliance on nonrenewable sources. Comparative analyses demonstrate the superiority of the machine learning-based approach in terms of accuracy and adaptability over traditional control methods.