Techno-Economic Evaluation of Hybrid Energy Systems Using Artificial Ecosystem-Based Optimization with Demand Side Management

Electrification of remote rural areas by adopting renewable energy technologies through the advancement of smart micro-grids is indispensable for the achievement of continuous development goals. Satisfying the electricity demand of consumers while adhering to reliability constraints with docile computation analysis is challenging for the optimal sizing of a Hybrid Energy System (HES). This study proposes the new application of an Artificial Ecosystem-based Optimization (AEO) algorithm for the optimal sizing of a HES while satisfying Loss of Power Supply Probability (LPSP) and Renewable Energy Fraction (REF) reliability indices. Furthermore, reduction of surplus energy is achieved by adopting Demand Side Management (DSM), which increases the utilization of renewable energy. By adopting DSM, 28.38%, 43.05%, and 65.37% were achieved for the Cost of Energy (COE) saving at 40%, 60%, and 80% REF, respectively. The simulation and optimization results demonstrate the most cost-competitive system configuration that is viable for remote-area utilization. The proposed AEO algorithm is further compared to Harris Hawk Optimization (HHO) and the Future Search Algorithm (FSA) for validation purpose. The obtained results demonstrate the efficacy of AEO to achieve the optimal sizing of HES with the lowest COE, the highest consistent level, and minimal standard deviation compared with HHO and FSA. The proposed model was developed and simulated using the MATLAB/code environment.

Analysis of Magnetohydrodynamic Perturbations in the Radial-field Solar Wind from Parker Solar Probe Observations

We report analysis of sub-Alfvénic magnetohydrodynamic (MHD) perturbations in the low-β radial-field solar wind employing the Parker Solar Probe spacecraft data from 2018 October 31 to November 12. We calculate wavevectors using the singular value decomposition method and separate MHD perturbations into three eigenmodes (Alfvén, fast, and slow modes) to explore the properties of sub-Alfvénic perturbations and the role of compressible perturbations in solar wind heating. The MHD perturbations show a high degree of Alfvénicity in the radial-field solar wind, with the energy fraction of Alfvén modes dominating (∼45%–83%) over those of fast modes (∼16%–43%) and slow modes (∼1%–19%). We present a detailed analysis of a representative event on 2018 November 10. Observations show that fast modes dominate magnetic compressibility, whereas slow modes dominate density compressibility. The energy damping rate of compressible modes is comparable to the heating rate, suggesting the collisionless damping of compressible modes could be significant for solar wind heating. These results are valuable for further studies of the imbalanced turbulence near the Sun and possible heating effects of compressible modes at MHD scales in low-β plasma.

Dissecting the collinear structure of quark splitting at NNLL

We explore the collinear limit of final-state quark splittings at order $$ {\alpha}_s^2 $$ α s 2 . While at general NLL level, this limit is described simply by a product of leading-order 1 → 2 DGLAP splitting functions, at the NNLL level we need to consider 1 → 3 splitting functions. Here, by performing suitable integrals of the triple-collinear splitting functions, we demonstrate how one may extract $$ {\mathrm{\mathcal{B}}}_2^q(z) $$ ℬ 2 q z , a differential version of the coefficient $$ {\mathrm{\mathcal{B}}}_2^q $$ ℬ 2 q that enters the quark form factor at NNLL and governs the intensity of collinear radiation from a quark. The variable z corresponds to the quark energy fraction after an initial 1 → 2 splitting, and our results yield effective higher-order splitting functions, which may be considered as a step towards the construction of NNLL parton showers. Further, while in the limit z → 1 we recover the standard soft limit results involving the CMW coupling with scale kt, the z dependence we obtain also motivates the extension of the notion of a physical coupling beyond the soft limit.

Evolution of turbulent kinetic energy during the entire sandstorm process

An adaptive segmented stationary method for non-stationary signal is proposed to reveal the turbulent kinetic energy evolution during the entire sandstorm process observed at the Qingtu Lake Observation Array. Sandstorm which is a common natural disaster is mechanically characterized by a particle-laden two-phase flow experiencing wall turbulence, with an extremely high Reynolds number and significant turbulent kinetic energy. Turbulence energy transfer is important to the understanding of sandstorm dynamics. This study indicates that large-/very-large-scale coherent structures originally exist in the rising stage of sandstorms with a streamwise kinetic energy of 75 % rather than gradually forming. In addition to carrying a substantial portion of energy, the very-large-scale-motions are active structures with strong nonlinear energy transfer. These structures gain energy from strong nonlinear interaction. As sandstorm evolves, these large structures are gradually broken by quadratic phase coupling, with the energy fraction reducing to 40 % in the declining stage. The nonlinear process in the steady and declining stages weakens and maintains a balanced budget of energy. The systematic bispectrum results provide a new perspective for further insight of sandstorms.

Resistance Analysis of Morphologies in Headwater Mountain Streams

River flow velocity is determined by the energy available for flow motion and the energy fraction lost by flow resistance. We compared the performance of different equations for the Darcy-Weisbach resistance coefficient (f) and empirical equations to predict flow velocity. The set of equations was tested using data from the Quinuas headwater mountain river in the Andean region. The data was collected in three Cascades, two Step-pools, and one Plane-bed covering a wide range of velocity magnitudes. The results reveal that nondimensional hydraulic geometry equations (NDHG) with a Nash-Sutcliffe efficiency index (EF) varying from 0.6–0.85 provide the most accurate velocity prediction. Furthermore, the study proposes a methodology applicable to all morphologies for defining the NDHG parameters using easily measured field data. The results show an improvement in predictability with EF values in the range of 0.81–0.86. Moreover, the methodology was tested against data from the literature, which was not divided into morphologies providing EF values of around 0.9. The authors encourage the application of the presented methodology to other reaches to obtain additional data about the NDHG parameters. Our findings suggest that those parameters could be related to reach characteristics (e.g., certain characteristic grain size), and in that case, the methodology could be useful in ungauged streams.

Performance Investigation of a Solar Photovoltaic/Diesel Generator Based Hybrid System with Cycle Charging Strategy Using BBO Algorithm

In the current scenario, sustainable power generation received greater attention due to the concerns of global warming and climate change. In the present paper, a Solar Photovoltaic/Diesel Generator/ Battery-based hybrid system has been considered to meet the electrical energy demand of a remote location of India. The cost of the energy of hybrid system is minimized using a Biogeography-based Optimization (BBO) algorithm under the constraints of power reliability, carbon emission and renewable energy fraction. Load following and cycle charging strategies have been considered in order to investigate the performance analysis of the proposed hybrid system. Further, different component combinations of specifications available on the market are presented for detail analysis. The minimum cost of energy of the proposed hybrid system is obtained as 0.225 $/kWh.