Academic literature on the topic 'Off-the-grid methods'

Gridless sparse spike reconstruction is a rather new research field with significant results for the super-resolution problem, where we want to retrieve fine-scale details from a noisy and filtered acquisition. To tackle this problem, we are interested in optimisation under some prior, typically the sparsity i.e., the source is composed of spikes. Following the seminal work on the generalised LASSO for measures called the Beurling-Lasso (BLASSO), we will give a review on the chief theoretical and numerical breakthrough of the off-the-grid inverse problem, as we illustrate its usefulness to the super-resolution problem in Single Molecule Localisation Microscopy (SMLM) through new reconstruction metrics and tests on synthetic and real SMLM data we performed for this review.

Off-grid algorithms for direction of arrival (DOA) estimation have become attractive because of their advantages in resolution and efficiency over conventional ones. In this paper, we propose a grid reconfiguration direction of arrival (GRDOA) estimation method based on sparse Bayesian learning. Unlike other off-grid methods, the grid points of GRDOA are treated as dynamic parameters. The number and position of the grid points are varied iteratively via a root method and a fission process. Then, the grid gets reconfigured through some criteria. By iteratively updating the reconfigured grid, DOAs are estimated completely. Since GRDOA has fewer grid points, it has better computational efficiency than the previous methods. Moreover, GRDOA can achieve better resolution and relatively higher accuracy. Numerical simulation results validate the effectiveness of GRDOA.

In this research paper, a pair of three-step hybrid block methods is derived for the solutions of linear and nonlinear first-order systems. The derivation is carried out with the aid of collocation and interpolation technique and the adoption of power series as basis function. The first and second three-step hybrid block methods are derived by incorporating a single and double off-grid point(s) respectively within the three-step integration interval. The methods derived were then applied on some linear and nonlinear first-order systems to test their accuracy and efficiency. The results obtained show that the three-step hybrid block method with two off-grid points performed better than the three-step hybrid block method with one off-grid point. It was also clear from the results obtained that the two methods derived performed better than the existing methods with which we compared our results. We further analyzed the basic properties of the methods derived. These properties include zero-stability, consistence, convergence and region of absolute stability.

The direction of arrival (DOA) estimation problem as an essential problem in the radar system is important in radar applications. In this paper, considering a multiple-input and multiple-out (MIMO) radar system, the DOA estimation problem is investigated in the scenario with fast-moving targets. The system model is first formulated, and then by exploiting both the target sparsity in the spatial domain and the temporal correlation of the moving targets, a sparse Bayesian learning (SBL)-based DOA estimation method combined with the Kalman filter (KF) is proposed. Moreover, the performances of traditional sparse-based methods are limited by the off-grid issue, and Taylor-expansion off-grid methods also have high computational complexity and limited performance. The proposed method breaks through the off-grid limit by transforming the problem in the spatial domain to that in the time domain using the movement feature. Simulation results show that the proposed method outperforms the existing methods in the DOA estimation problem for the fast-moving targets.

Taking into consideration the lack of circumstantial alertness, automated fault analysis and labor-saving switches, the present-day electrical power grid system has been deteriorating day by day. The backbone technology of this grid system is too ill-fitted to the on-going demand for electricity. Despite the fact that the government of Bangladesh has set a new target of reaching the total power generation to be 40,000 MW by 2030. Hence the infrastructure and corresponding technology of the electrical power sector are required to be modernized to cope with this gigantic target within a short time. Another challenging fact is that the rapid expansion of population and power-intensive industrialization trigger off the carbon emissions that lead to global climate change. Also, the constraints of electricity generation capacity, unidirectional way of communication, failure of power equipment and dropping off conventional sources of energy impose burden on the existing electric power grid. This paper articulates the needfulness of reflection on CO2 emissions or reduction in the electricity bill of the consumer in developing countries by employing a mathematical model and by proposing some fruitful methods to implement AMI for smart grid.

Radar coincidence imaging (RCI) is a high-resolution staring imaging technique without the limitation of the target relative motion. To achieve better imaging performance, sparse reconstruction is commonly used. While its performance is based on the assumption that the scatterers are located at the prediscretized grid-cell centers, otherwise, off-grid emerges and the performance of RCI degrades significantly. In this paper, RCI using frequency-hopping (FH) waveforms is considered. The off-grid effects are analyzed, and the corresponding constrained Cramér-Rao bound (CCRB) is derived based on the mean square error (MSE) of the “oracle” estimator. For off-grid RCI, the process is composed of two stages: grid matching and off-grid error (OGE) calibration, where two-dimension (2D) band-excluded locally optimized orthogonal matching pursuit (BLOOMP) and alternating iteration minimization (AIM) algorithms are proposed, respectively. Unlike traditional sparse recovery methods, BLOOMP realizes the recovery in the refinement grids by overwhelming the shortages of coherent dictionary and is robust to noise and OGE. AIM calibration algorithm adaptively adjusts the OGE and, meanwhile, seeks the optimal target reconstruction result.

This study employs a mixed-methods research approach to scrutinize the varied determinants influencing the willingness of off-grid rural households in Kwara State, Nigeria, to adopt Solar Home Systems (SHS). Integrating quantitative survey data from 400 households, qualitative findings derived from semi-structured interviews, and secondary data, the research provides a robust empirical framework. Through the application of Interval Regression and Tobit models, the analysis pinpoints income and education as key positive drivers towards SHS adoption, yet reveals a contrasting gender divide. Interestingly, a high level of satisfaction with the current energy provision emerges as a stumbling block to SHS acceptance. The research further identifies a distinct trend: households located further away from the grid exhibit a heightened propensity to pay for SHS, signifying a higher value attribution to SHS in these off-grid areas. The findings underscore the need for targeted interventions that encapsulate the diverse characteristics of households to ensure successful SHS promotion. The comprehensive insights garnered from this study offer indispensable guidance for policymakers and energy providers, bolstering strategic efforts to enhance SHS uptake and ultimately contributing to Nigeria's shift towards a more sustainable energy future.

Direction of arrival (DOA) estimation is an essential and fundamental part of array signal processing, which has been widely used in radio monitoring, autonomous driving of vehicles, intelligent navigation, etc. However, it remains a challenge to accurately estimate DOA for multiple-input multiple-output (MIMO) radar in impulsive noise environments. To address this problem, an off-grid DOA estimation method for monostatic MIMO radar is proposed to deal with non-circular signals under impulsive noise. In the proposed method, firstly, based on the property of non-circular signal and array structure, a virtual array output was built and a real-valued sparse representation for the signal model was constructed. Then, an off-grid sparse Bayesian learning (SBL) framework is proposed and further applied to the virtual array to construct novel off-grid sparse model. Finally, off-grid DOA estimation was realized through the solution of the sparse reconstruction with high accuracy even in impulsive noise. Numerous simulations were performed to compare the algorithm with existing methods. Simulation results verify that the proposed off-grid DOA method enables evident performance improvement in terms of accuracy and robustness compared with other works on impulsive noise.

Grid-connected inverter topologies and control methods are analyzed and compared on the basis of two non-isolated PV grid-connected inverter circuit topology as 3kVA grid connected PV systems. In this paper, the current control method, the DC bus voltage outer grid current inner loop of the dual closed-loop PI control method. Sampled signal through the system circuit for processing, resulting in a SPWM drive signal to control the conduction of the power devices off the grid current control.

Abstract In this paper, Hybrid Block Adams Moulton Methods for step number k = 2 merged with two and three off-grid points were obtained and implemented in solving first order delay differential equations without the use of interpolation condition in evaluating the delay expression. The discrete schemes of these off-grid hybrid block methods were assessed through the continuous development of the linear multistep collocation method using a matrix conversion formula. The results obtained after the implementation of the proposed method in for numerical experiment of some first-order DDEs, the BHAMM2 schemes performed better and faster in satisfying the axioms for convergence and region of absolute stability than the BHAMM3 schemes at fixed step size z when examined with other existing methods.

Cette thèse de doctorat se propose de définir et d'expliciter les outils pour la reconstruction de courbes par des méthodes variationnelles sans-grille dans une perspective d'applications en imagerie biomédicale, et plus précisément en microscopie de fluorescence.Plusieurs algorithmes numériques convexes ou non-convexes ont déjà été développés au sein de l'équipe de recherche Morpheme qui a accueilli cette thèse, dans le cadre de l'optimisation non convexe parcimonieuse. Ces techniques ont été développées dans le cas discret, sur grille, ce qui signifie que l'image super-résolue est définie sur une grille plus fine que les images observées. A contrario, dans cette thèse de doctorat, nous développons des méthodes sans grille, en travaillant sur l'espace des mesures bornées. Ce choix apporte plusieurs avantages : la source n'est pas limitée par une grille fine et peut être localisée continûment, des bornes quantitatives sur l'écart entre la reconstruction et la source peuvent être énoncées, et ces approches permettent d'encoder une information structurelle ou géométrique dans le problème variationnel, de sorte à reconstruire une source d'une certaine forme, telle qu'un point ou une courbe.Dans un premier temps, nous rappellerons l'état de l'art des méthodes sans-grille, qui porte sur la reconstruction de sources ponctuelles. Nous illustrerons en particulier son intérêt pratique en microscopie de fluorescence, notamment face à des modèles classiques : les reconstructions proposées sont aussi convaincantes que celles produites par des modèles d'apprentissage profond, et ce, sans nécessiter d'ensemble d'apprentissage ou d'hyperparamètres à régler.Puis, nous introduirons une méthode sans-grille dans le cadre de la microscopie de fluorescence par fluctuation. Cette dernière repose sur l'indépendance des fluctuations de luminosité des fluorophores, ce qui permet en pratique de faire l'économie de matériel d'acquisition spécifique et coûteux. Les limites de ce modèle, formulé pour la reconstruction de sources ponctuelles, nous conduirons à nous intéresser sur la formulation d'une nouvelle méthode sans-grille, cette fois-ci pour la reconstruction de sources courbes.Nous nous pencherons sur la conception d'une méthode sans-grille, qui répond à la problématique de fluorophores le long de filaments qui forment de courbes dans les images, et qui n'est à notre connaissance pas traitée dans la littérature. En particulier, nous définirons l'espace d'optimisation impliqué, à savoir l'espace des mesures vectorielles à divergence finie, aussi appelé espace des charges. Nous poursuivrons en explicitant la manière de définir des mesures portées par des courbes, et en écrivant une nouvelle fonctionnelle appelée CROC (pour Curves Represented On Charges). Nous finirons en montrant qu'il existe une solution de cette énergie qui s'écrit comme une combinaison linéaire finie de mesures portées par des courbes.Finalement, nous étudierons la mise en œuvre numérique de la minimisation de cette énergie, à l'aide d'une adaptation d'algorithme glouton répandu dans la littérature. Nous discuterons des propriétés de notre algorithme, appelé Charge Sliding Frank-Wolfe, des difficultés rencontrées dans la définition du modèle d'observation, dans le choix de la paramétrisation discrète des courbes, etc.Le propos sera clôturé avec une conclusion qui rappellera les principaux points de chaque chapitre, ainsi qu'une ouverture, récapitulant les pistes de recherche possibles que nous avons dégagées au fil des chapitres
This PhD thesis aims to define and explain the tools for curve reconstruction using off-the-grid variational methods, as part of a range of applications in biomedical imaging, and more specifically in fluorescence microscopy.Several convex or non-convex numerical algorithms have already been developed within the Morpheme research team, where this thesis was conducted, in the context of non-convex sparse optimisation. These techniques were developed in the discrete case, on a grid, which means that the super-resolved image is defined on a finer grid than the observed images. In contrast, in this PhD thesis, we are developing off-the-grid also called gridless methods, while working on the space of bounded measures. This choice brings several advantages: the source is not limited by a fine grid and can be located continuously, quantitative bounds on the discrepancy between the reconstruction and the source can be written, and these approaches allow encoding structural/geometric information in the variational problem, to recover a source of a certain shape, such as a point or a curve.We will begin by reviewing the state of the art in off-the-grid methods, focusing on the reconstruction of point sources. In particular, we will illustrate its practical interest in fluorescence microscopy, especially compared with classical models: the reconstructions proposed are as convincing as those produced by deep learning models, and this without requiring a learning set or hyperparameters tuning.We will then introduce a gridless method for fluctuation-based fluorescence microscopy. This method exploits the independence of fluctuations in fluoroform luminosity, which means that specific and expensive acquisition equipment is not required. The limitations of this model, formulated for the reconstruction of point sources, will lead us to define a new off-the-grid method, this time for the reconstruction of curve.We will further study the design of an off-the-grid method, addressing the struggle of fluoroform along filaments that form curves in the images; a problem which is not taken care of in the literature. In particular, we will define the involved optimisation space, namely the space of vector measures with finite divergence, also known as the space of charges. We will further explain how to define curve measures, and we will write a new functional called CROC (for Curves Represented On Charges). We will finish by showing that there is a solution to this energy that can be written as a finite linear combination of measurements carried by curves.Finally, we will study the numerical implementation of this problem, using an adaptation of the greedy algorithm widely used in the literature. We will discuss the properties of our algorithm, called Charge Sliding Frank-Wolfe, and the difficulties encountered in the definition of the observation model, in choosing the discrete parametrisation of the curves, and so on.The thesis will be concluded with a reminder of the main points encountered in each chapter, and an opening section summing up the possible avenues of research that we have identified over the course of the chapters

The United Nations (UN) project "Sustainable energy for all" sets three ambitious objectives to favor a sustainable development and to limit climate change: - Universal access to modern energy services. Electricity is currently not available for 1.3 billion people and the global energy demand is expected to grow of about 35% within 2040, due to the increasing world population and the expanding economies - Double the global rate of improvement in energy efficiency - Double the share of renewable energy sources (RESs) in the global energy mix In addition, according to the climate scenario assessed in the fifth assessment report (AR5) of the International Panel on Climate Change (IPCC), the prevention of undesirable climate effects requires a 40-70% reduction of greenhouse gas (GHG) emissions, compared with 2010 levels, by mid-century, and to near-zero by the end of this century (IPCC, 2014). The achievement of such objectives requires and encourages the spread of RESs in the global energy mix, gradually replacing depleting and polluting energy sources based on fossil fuels, which still have the main incidence on the energy sector. RESs already play a major role in several countries, due to the technological development and the increasing market competitiveness, and the world renewable power capacity reached 22.1% in 2013, showing an increasing trend in 2014 (REN, 2014). However, supporting policies, robust investments from the private sector and efforts from the scientific community are still crucial to demonstrate the technical and economic sustainability and effectiveness of RESs, helping their large-scale diffusion. Starting from such a background, this Ph.D dissertation focuses on the study, design and development of methods and tools for the optimization and enhancement of renewable energy technologies and their effective integration with energy storage solutions and traditional energy sources powered by fossil fuels (hybrid energy systems). The analysis of the major literature and the different scenarios and perspectives of RESs in the national and international contexts have shown that their economic sustainability, and then their diffusion, is closely connected to a number of technical, economic/financial and geographical parameters. Such parameters are the input of the analytic models developed for the techno-economic design of photovoltaic (PV) plants and small wind turbines (SWTs) and applied to the economic feasibility study, through multi-scenario analysis, of such systems in some of the main European Union (EU) Countries. Among the obtained results, the self-consumption of the produced energy plays a crucial role in the economic viability of SWTs and PV plants and, particularly, after the partial or total cut of incentives and uncertainties related to supporting policies within the EU context. The study of the energy demand profile of a specific user and the adoption of battery energy storage (BES) systems have been identified as effective strategies to increase the energy self-consumption contribution. Such aspects have led to the development of an analytic model for the techno-economic design of a grid connected hybrid energy system (HES), integrating a PV plant and a BES system (grid connected PV-BES HES). The economic profitability of the grid connected PV-BES HES, evaluated for a real case study, is comparable with PV plants without storage in case of a significant gap between the cost of energy purchased from the grid and the price of energy sold to the grid, but high BES system costs due to the initial investment and the maintenance activities and the eventual presence of incentives for the energy sold to the grid can make the investment not particularly attractive. Thus, the focus has shifted to the techno-economic analysis of off-grid HES to meet the energy demand of users in remote areas. In this context, BES systems have a significant role in the operation and management of the system, in addition to the storage of exceeding energy produced by the intermittent and variable RESs. The analysis has also been strengthened by an industrial application with the aim to configure, test and install two off-grid HESs to meet the energy demand of a remote village and a telecommunication system. In parallel, two experimental activities in the context of solar concentrating technology, a promising and not fully developed technology, have been carried out. The former activity deals with the design, development and field test of a Fresnel lens pilot-scale solar concentrating prototype for the PV energy distributed generation, through multi-junction solar cells, and the parallel low temperature heat recovery (micro-cogeneration CPV/T system). The latter activity deals with the development of a low cost thermal energy (TES) storage prototype for concentrating solar power (CSP) plants. TES systems show a great potential in the CSP plants profitability since they can overcome the intermittent nature of sunlight and increase the capacity factor of the solar thermal power plant. Concluding, the present Ph.D dissertation describes effective methods and tools for the optimization and enhancement of RESs. The obtained results, showing their critical issues and potential, aim to contribute to their diffusion and favor a sustainable development
Il progetto delle Nazioni Unite "Sustainable energy for all" ha fissato tre obiettivi ambiziosi per favorire uno sviluppo sostenibile e limitare l'impatto del cambiamento climatico: - Accesso universale a moderni servizi elettrici. Tali servizi sono attualmente indisponibili per circa 1.3 miliardi di persone ed è previsto un aumento del 40% della domanda globale di energia elettrica entro il 2040, a causa dell'incremento della popolazione mondiale e delle economie in crescita nei paesi in via di sviluppo - Raddoppio del tasso globale di miglioramento dell'efficienza energetica - Raddoppio del contributo di fonti di tipo rinnovabile nel mix energetico globale Inoltre, lo scenario climatico proposto nel "fifth assessment report (AR5)" redatto da "International Panel on Climate Change (IPCC)" stabilisce la necessità di ridurre l'emissione di gas ad effetto serra del 40-70%, rispetto ai valori registrati nel 2010, entro il 2050 ed eliminarli in modo quasi definitivo entro la fine del secolo con lo scopo di evitare effetti climatici indesiderati. Il raggiungimento di tali obiettivi richiede e incoraggia la diffusione di fonti energetiche rinnovabili (FER) all'interno del mix energetico globale, rimpiazzando gradualmente le fonti di energia convenzionali basate su combustibili fossili, inquinanti e in via di esaurimento, che hanno ancora l'incidenza principale nel settore energetico. A seguito nel loro sviluppo tecnologico e la crescente competitività nel mercato, le FER rivestono già un ruolo fondamentale nel mix energetico di numerose Nazioni ricoprendo il 22.1% del fabbisogno globale di energia nel 2013 e mostrando un andamento in rialzo nel 2014 (REN, 2014). Tuttavia, sono ancora cruciali politiche di supporto, ingenti investimenti privati e contributi della comunità scientifica per dimostrare l'efficacia e la sostenibilità tecnica ed economica delle FER e favorire, quindi, una loro diffusione in larga scala. In questo contesto, la seguente tesi di dottorato è rivolta allo studio, progettazione e sviluppo di metodi e strumenti per l'ottimizzazione e la valorizzazione di tecnologie energetiche rinnovabili e la loro integrazione efficace con fonti di produzione di energia convenzionali alimentate da combustibili fossili e sistemi di accumulo di energia (Sistemi energetici di tipo ibrido). I contributi scientifici disponibili in letteratura e l'analisi dei diversi scenari e delle prospettive delle FER nei vari contesti nazionali ed internazionali hanno dimostrato che la loro sostenibilità economica, e quindi la loro diffusione, è strettamente legata ad una serie di parametri tecnici, economico / finanziari e geografici. Tali parametri sono stati impiegati come input in due modelli analitici sviluppati per la progettazione tecnico-economica di impianti fotovoltaici (FV) e micro turbine eoliche e applicati per lo studio della loro fattibilità economica, attraverso analisi multi-scenario, in alcuni dei maggiori Paesi Europei. I risultati ottenuti hanno mostrato come l'autoconsumo dell'energia prodotta rivesta un ruolo fondamentale nella redditività economica dei citati impianti ed, in particolare, a seguito del taglio parziale o totale dei sistemi di incentivazione e l'incertezza attorno alle politiche di supporto all'interno del panorama Europeo. Lo studio specifico del profilo di domanda elettrica delle utenze e l'impiego di sistemi di accumulo di energia sono stati identificati come strategie efficaci al fine di incrementare la quota di autoconsumo. Tali considerazioni hanno portato allo sviluppo di un modello analitico utile alla progettazione tecnico-economica un sistema energetico ibrido connesso alla rete Nazionale integrante un impianto FV e un sistema di accumulo a batterie. La redditività del sistema, valutata su un caso reale, risulta comparabile a un impianto fotovoltaico privo di batterie in caso di un gap significativo tra il costo dell'energia elettrica acquistata dalla rete e il prezzo di vendita dell'energia elettrica ceduta in rete. Tuttavia, gli elevati costi dovuti all'acquisto iniziale e alle attività di manutenzione, e l'eventuale incentivazione sulla vendita dell'energia in rete, non rendono l'investimento particolarmente attrattivo per impianti connessi alla rete. L'attenzione si è quindi rivolta all'analisi tecnico-economica di sistemi energetici ibridi non connessi alla rete, comunemente definiti in isola o off-grid, per soddisfare il fabbisogno energetico di utenti in area remote e quindi prive di allaccio a una rete elettrica. In tali sistemi, i sistemi di accumulo a batterie, oltre alla capacità di accumulo dell'energia prodotta in eccesso variabili e intermittenti FER, hanno funzioni fondamentali nella gestione del sistema stesso. L'attività è stata anche rafforzata da un'applicazione industriale per la configurazione, test e installazione di due sistemi energetici ibridi in isola impiegati per soddisfare il fabbisogno energetico di un villaggio e di un sistema di telecomunicazione situati in aree remote. In parallelo, sono state svolte due attività sperimentali applicate alla promettente, ma non ancora completamente sviluppata a livello industriale, tecnologia solare a concentrazione. La prima attività riguarda la progettazione, sviluppo e test sperimentali di un prototipo in scala ridotta di concentratore solare a lenti di Fresnel per la produzione distribuita di energia elettrica, mediante l'uso di celle fotovoltaiche multi giunzione, ed energia termica a bassa temperatura, tramite un sistema di recupero termico. La seconda attività concerne lo sviluppo e test sperimentali di un prototipo di sistema di accumulo termico per impianti termodinamici alimentati da sistemi a concentrazione solare. Il sistema di accumulo consente di compensare la natura intermittente e variabile della fonte solare incrementando le ore di funzionamento dell'impianto termodinamico con i conseguenti benefici economici. Concludendo, la presente tesi di dottorato include la descrizione di metodi e strumenti per l'ottimizzazione e valorizzazione delle FER. I risultati evidenziano le criticità e potenzialità dei sistemi studiati con lo scopo di contribuire a una loro diffusione e favorire uno sviluppo sostenibile

AbstractMulti-electrolyzers system is an effective method to address the problem that the lowest operating point of the alkaline water electrolyzer still is high when the water electrolysis system is coupled with renewable energy. This work proposed different configurations of nominal power and operating strategies of electrolyzers for an off-grid isolated stand-alone wind hydrogen system. The configurations contain different nominal power of electrolyzers rather than the same nominal power. An equal load strategy is proposed and simulated based on the operation characteristics of the alkaline electrolyzer. This strategy could reach the 99% of energy absorption rate.

This paper introduces parallel computation for spread options using two-dimensional Fourier transform. Spread options are multi-asset options whose payoffs depend on the difference of two underlying financial securities. Pricing these securities, however, cannot be done using closed-form methods; as such, we propose an algorithm which employs the fast Fourier Transform (FFT) method to numerically solve spread option prices in a reasonable amount of short time while preserving the pricing accuracy. Our results indicate a significant increase in computational performance when the algorithm is performed on multiple CPU cores and GPU. Moreover, the literature on spread option pricing using FFT methods documents that the pricing accuracy increases with FFT grid size while the computational speed has opposite effect. By using the multi-core/GPU implementation, the trade-off between pricing accuracy and speed is taken into account effectively.

In the contemporary era of technological advancements, solar energy emerges as a promising and easily implementable solution to meet future energy demands sustainably. This chapter delves into recent innovative techniques and simulation software pertaining to this environmentally friendly technology, focusing on device simulation, novel structures, and cutting-edge methods. A comparative analysis among major solar cell modeling simulators, such as PC1D, SCAPS-1D, wxAMPS-1D, AMPS-1D, ASA, Gpvdm, SETFOS, PECSIM, ASPIN, ADEPT, AFORS-HET, TCAD, and SILVACO ALTAS, is presented. These simulators not only aid in analyzing fabricated cells but also predict the impact of device modifications. The current year has witnessed significant efforts in developing sustainable energy systems through innovative solar cell simulators and semiconductor models. A concise evaluation of well-established solar cell simulators is provided to identify the most reliable tool for assessing photovoltaic technology performance. The chapter offers a user-friendly linear operating procedure and a congenial dialog box for multi-junction solar cells, providing valuable benefits for scientists, researchers, and skilled programmers in the photovoltaic community. This solar simulation software plays a crucial role in designing environment-friendly solar energy systems and calculating potential solar PV system outcomes for various projects, both grid-tied and off-grid, continually improving the solar energy technology landscape.

With the rapid development of the science and technology era, digital technology has been widely used in power grid infrastructure projects, and intelligent equipment, high-precision sensing technology, and wireless ad hoc network equipment have been used in various fields of power grid infrastructure. Nowadays, the pay-off construction of overhead transmission lines has also realized intelligent visualization of centralized control, which has improved the safety of stringing construction. Overhead tension pay-off is a key infrastructure construction project, and for the safety of overhead tension pay-off, there are major breakthroughs in reducing labor costs, improving the automation degree of tension pay-off, and improving the working environment, which effectively improves safety construction efficiency. In this paper, the long-term and short-term memory network model using deep learning is used to model and quantitatively analyze the overhead tension pay-off, predict its future security risks, take timely measures, and adjust the operation strategy according to the prediction results of the model.

With the rapid growth of distributed generation currently, DC microgrids energy system structure is being deployed in parallel with, or independently from, the main power grid network. The DC microgrids energy system structure is designed to provide an effective coordination with the aggregating distributed generators, energy storage, and connected loads. In this sense, the DC microgrids energy system structure can be connected to the grid network or can be off-grid network. In the mode of grid network connected, DC microgrids energy system structure is presented as a controllable entity. When it is necessary, DC microgrids energy system is connected in islanded mode to deliver reliable power to the grid network during the interrupted power supply from the grid network system. Having said that, the DC microgrids energy system structure is encompassed of renewable energy sources, energy storages and loads, and not excluding the grid network transmission. Hence, this chapter proposes to focus on designing and modelling a self-assistive controller using voltage droop method for DC distributed generators and storages which is a part of the DC microgrids energy system structure.

In order to improve the voltage unbalance of large-scale wind power collection grid three-phase and solve the problem that negative sequence voltage unbalance factor exceeds the national standard, which sometimes even causes the wind turbine generators to be off grid, this paper proposes a split-phase flow optimization method that uses the split-phase reactive power compensation equipments in wind farms collected area to improve the voltage unbalance. Firstly, this paper takes the collection grid and the wind farms as optimization nodes, establishes an optimization model to minimize the mean value of voltage unbalance degree, then the node voltage safety constraint and the constraint condition of the reactive power compensation capacity of each phase of the reactive power compensation equipments are considered. Secondly, this paper introduces PSO to solve the optimization problem. Finally, an actual area wind farms collected is used to verify that the method proposed in this paper can effectively solve the problem that voltage unbalance degree is too high.

This paper presents a grid scheduling model to schedule a job on the grid with the objective of ensuring maximum reliability to the job under the current grid state. The model schedules a modular job to those resources that suit the job requirements in terms of resources while offering the most reliable environment. The reliability estimates depict true grid picture and considers the contribution of the computational resources, network links and the application awaiting allocation. The scheduling executes the interactive jobs while considering the looping structure. As scheduling on the grid is an NP hard problem, soft computing tools are often applied. This paper applies Modified Genetic Algorithm (MGA), which is an elitist selection method based on the two threshold values, to improve the solution. The MGA works on the basis of partitioning the current population in three categories: the fittest chromosomes, average fit chromosomes and the ones with worst fitness. The worst are dropped, while the fittest chromosomes of the current generation are mated with the average fit chromosomes of the previous generation to produce off-spring. The simulation results are compared with other similar grid scheduling models to study the performance of the proposed model under various grid conditions.

One of Myanmar's problems is energy shortage. Partially, energy shortage can possibly be decreased by the construction of sizeable grid-connected offshore wind farms. Eight prospective construction sites were selected and wind turbine models chosen. This chapter describes the method for determining the optimal composition of the wind farms complex, consisting of several offshore wind farms located at a considerable distance from each other in areas with significant wind regime asynchrony. To illustrate this method, the optimal composition with an installed capacity of 47.6 MW and located off Myanmar's west coast is defined.

In order to realize grid connection and transient real-time simulation of DFIG, a multi-rate parallel real-time simulation method based on co-simulation of Field Programmable Gate Array (FPGA) and CPU is studied. The switching function method is used to model the “back-to-back” converters in the DFIG and optimize the resources, and the modeling segmentation between systems with different step is completed based on PWM averaging to realize multi-rate parallel simulation. The small-step simulation of the “back-to-back” converter is realized with an operational step of 200 by use of high operational rate and strong parallelism of FGPA. Facing the needs of real-time simulation of active distribution network, the 200ns/50 parallel co-simulation of FPGA and independent parallel real-time simulator UREP is realized. By comparing with the off-line simulation results of Simulink and analyzing FGPA resource consumption rate before and after model optimization in this paper, the real-time capacity and accuracy of research method in the paper are verified, which provide a reliable technical support for grid connection and transient research of all kinds of wind power systems.

Abstract A prediction of radiative heat transfer in a complex geometry was performed using different boundary treatments such as blocked-off, spatial-multiblock, and embedded boundary methods. The formulation of embedded boundary treatment for finite volume was derived here. The finite-volume method (FVM) was used to model the radiative transfer in an absorbing and emitting medium which is maintained at an isothermal condition and enclosed by cold and black walls. While the body-fitted grid system was used for the spatial multi-block treatment, the Cartesian grid system was chosen for the others. Their results were compared and discussed for three different cases including trapezoidal enclosure, semi-circular enclosure with internal block, and incinerator-shaped enclosure. The accuracy obtained by application of each treatment was shown to be highly satisfactory. Consequently, each treatment was suitable for modeling the radiative heat transfer in the complex geometry. However, the solution obtained by the blocked-off treatment has yielded some errors compared with the others, since the Cartesian grid used in the blocked-off treatment could not exactly configure the complex boundaries. Especially, the radiative heat flux on the non-orthogonal wall was largely underestimated due to its stepwise description of the wall.

One of the primary challenges for wave energy converter (WEC) systems is the fluctuating nature of wave resources, which require the WEC components to be designed to handle loads (i.e., torques, forces, and powers) that are many times greater than the average load. This approach requires a much greater power take-off (PTO) capacity than the average power output and indicates a higher cost for the PTO. Moreover, additional design requirements, such as battery storage, are needed, particularly for practical electrical grid connection, and can be a problem for sensitive equipment (e.g., radar, computing devices, and sensors). Therefore, it is essential to investigate potential methodologies to reduce the overall power fluctuation while trying to optimize the power output from WECs. In this study, a detailed hydraulic PTO model was developed and coupled with a time-domain hydrodynamics model (WEC-Sim) to evaluate the PTO efficiency for WECs and the trade-off between power output and fluctuation using different power smoothing methods, including energy storage, pressure relief mechanism, and a power-based setpoint control method. The study also revealed that the maximum power fluctuation for WECs can be significantly reduced by one order of magnitude when these power smoothing methods are applied.

A finite-difference based approach for computing flows with complex moving solid three-dimensional boundaries on fixed Cartesian grid has been developed. Internal solid boundaries are represented by “blocking off” the grid cells inside the boundary. This results in considerably increased computing efficiency over conventional body-conformal structured grid methods. A mixed explicit-implicit fractional step method is employed for time integration while the spatial discretization scheme is based on a second-order accurate central-difference scheme. The pressure Poisson equation is solved using algebraic multigrid as well as Krylov subspace based methods. The current simulation methodology is validated by simulating various canonical flows. Further, we compute the flow generated by a moving body as well as the flow generated by a synthetic jet in order to demonstrate the capabilities of this solver.

A Reynolds-Averaged Navier-Stokes (RANS) method has been employed in conjunction with an overlapping moving grid approach to provide accurate resolution of four-quadrant propeller flows under both the design and off-design conditions. It is well known that some off-design propeller flow phenomena are dominated by viscous effects and cannot be accurately predicted by the potential flow methods. In order to properly account for viscous effects, it is necessary to employ accurate and robust numerical methods which can provide detailed resolution of the propeller boundary layer, turbulent wake, leading-edge separation, and unsteady ring vortices induced by propeller operations under off-design conditions. In this study, time-domain simulations are performed for the DTRC 4I18 propeller under ahead, bollard pull, crash-ahead, crash-astern, and backing conditions and compared with the available experimental data.

Coal consumption accounted for 36% of America’s CO2 emissions in 2005, yet because coal is a relatively inexpensive, widely available, and politically secure fuel, its use is projected to grow in the coming decades [1]. In order for coal to contribute to the U.S. energy mix without detriment to an environmentally acceptable future, implementation of carbon capture and sequestration (CCS) technology is critical. Techno-economic studies of CCS have demonstrated its large expense due to substantial energy requirements and capital costs. However, such analyses typically calculate cost indicators using static plant performance parameters that are assumed to be constant over plant lifetime. That is, CO2 capture systems are generally assumed to capture a constant percentage of CO2 from power plant flue gas and consume a particular amount of plant gross generation capacity. Such studies do not consider dynamic plant operation that may result from diurnal and seasonal variations in electricity supply and demand, nor do they capture the economic desire to minimize CO2 emissions costs while maximizing profits by selling electricity at high price times. In this study, CO2 capture systems are analyzed in a grid level dynamic framework by considering the possibility of turning capture systems off during peak system load to regain generation capacity lost to the energy requirement of CO2 capture. This practice eliminates the costs of building additional generation capacity to make up for CO2 capture energy requirements, and it allows plant operators to benefit from selling more electricity during high price time periods. Dynamic CO2 capture operation is particularly suited to post-combustion (PC) CO2 absorption, a leading capture technology that, unlike other capture methods, offers the ability for flexible or on/off operation. This paper presents a case study on the Electric Reliability Council of Texas (ERCOT) electric grid of baseline cost and CO2 emissions estimates associated with different strategies of using on/off CO2 capture operation to satisfy peak electricity demand. It compares base cases of no CO2 capture and “always on” capture with scenarios where capture is turned off during: 1) peak load hours every day of the year, 2) days of the year of system peak load, and 3) system peak load hours only on seasonal peak load days. The study considers the implications of installing PC CO2 capture on all coal-fired plants in the ERCOT grid to better understand if on/off operation is desirable and which operational strategy may be the most economically viable under a policy of constrained CO2 emissions.

In this study, unsteady flow field of an industrial double-suction centrifugal pump has been solved to obtain the interaction between impeller and volute casing. Quasi-steady and unsteady methods have been used. A block-structured grid is employed to represent the complicated pump geometry. The velocity field and the pressure fields of the pump are analyzed for the rated point and off-design points. Magnitude of the interaction is evaluated numerically.

Abstract Hybrid Lagrangian-Eulerian solvers combine the convective and compactness advantages of vortex methods with the spatial anisotropy and boundary-resolving advantages of Eulerian methods to create flexible solvers capable of adequately capturing thin boundary layers while still maintaining wake vortex coherency for unsteady incompressible flow in complex geometries. The present paper details a new hybrid method which combines, in one open-source package, a novel, compact, high-order Eulerian scheme for vorticity transport to predict the flow in the near-boundary region with a grid-free, unremeshed, Lagrangian Vortex Particle Method (LVPM) for the off-boundary vorticity-containing region. This paper focuses on the hybridization of the two methods and demonstrates its effectiveness on two canonical benchmarks: flow in 2-D lid-driven cavity at Re = 1,000 and impulsively started flow over a circular cylinder at Re = 9,500. In each case, the hybrid method improves upon a pure LVPM and uses far fewer cells than a purely Eulerian scheme. In addition, the size of the associated Eulerian region is greatly reduced compared to previous hybrid methods.

Hybrid RANS-LES methods are gaining popularity for the simulation of the complex bluff body flows at high Reynolds numbers due to their reduced computational cost and good accuracy. A number of such methods have been proposed in the literature. Each of these methods have enjoyed varying degree of success for different applications. One of the most important parameter which determines the switching between near-wall RANS region and off-body LES region is the length scale parameter. This parameter can be grid based or physics based and numerous choices exist for defining this parameter. This study proposes to investigate the effect of this parameter on the size of the RANS and LES regions and also on the solution accuracy. Four test problems are chosen covering attached, mildly separated and massively separated flow regimes. Results will help us to identify length scale definitions to be used for different flow scenarios.

Grid synchronization is a critical concern for proper control of the energy transferred between the Distributed Power Generation System (DPGS) and the utility grid. Nonlinear estimation techniques are proposed to track the voltage magnitude, phase angle, and frequency of the utility grid. Instead of directly analyzing in abc coordinate frame, the symmetrical component is employed to separate the positive, negative, and zero sequences in the transformed αβ stationary coordinate frame. By using αβ stationary coordinate frame, the number of system state variables is reduced to five. The results show that our proposed nonlinear estimation technique is efficient in smart power system synchronization. The MATLAB simulation studies have been conducted to compare the performance of Extended Kalman Filter (EKF), Particle Filter (PF), and Unscented Kalman Filter (UKF). Computer simulations have shown that the efficacy of our proposed nonlinear estimation methods including the Extended Kalman Filter, Particle Filter, and Unscented Kalman Filter. It also shows that the Unscented Kalman Filter, and the Particle Filters are better estimators, because voltage synchronization problem is nonlinear system, and linearization process which the Extended Kalman Filter is based on is not very accurate. The number of particles in Particle Filter can be increased to improve the accuracy, but there exists a trade off between computational effort and estimation accuracy. In our research, with consideration the same computational complexity, we calculate the Mean Square Error (MSE) to examine the performances of different nonlinear estimation approaches. By comparing the MSE of different estimators, we prove that the Unscented Kalman Filter shows the most accurate performance in voltage synchronization for three phase unbalanced voltage. Our results have shown the potential applications in the future smart power grid synchronization.

Increasing legislation and public awareness of noise are intensifying the efforts of industries today to reduce the noise. The natural gas industry has proved that it is one of the industrial leaders in its awareness of noise problems and has maintained a vigorous research program in noise control for over 30 years. A noise survey can be done in several ways: point measurements, grid point methods, contour methods, scanning techniques, etc. The selection of the method depends on the accuracy required, available personnel, expertise etc. For the most accurate determination of sound power, the scanning method using sound intensity measurements is considered to be the best available in present day circumstances. This method is discussed in detail in later chapters. Point and grid point measurements are useful to determine the Sound Pressure Level, but are of limited use in quantifying the Sound Power Level of a noise source in a complex and multi-source environment such as a compressor station. Guidelines for Sound Power Level Measurements for Compressor Station Equipment Report documents the development of guidelines for in-situ sound power level measurements for compressor station equipment, with sample calculations. Measurement of equipment noise levels in a complex, multi-source environment is very difficult and may be accomplished by several methods. These guidelines specify the sound intensity approach that can be used in almost any field situation. The sound power guidelines described in this report specify the sound intensity approach as the primary measurement method since it can be used in almost any field situation to determine the sound power of a source. In open spaces without reflecting surfaces (except the ground plane) sound pressure measurements may give satisfactory estimates of the sound power of noise sources if background noise is low and other sources can be turned off. Inside a compressor building, the modified reverberation room approach may be allowed, but then only the total sound power can be determined unless background sources can be controlled or other sources turned off. Lastly, the standard guidelines developed were used to conduct field measurements of the sound power of four equipment noise sources including: a) turbine casing, b) turbine air inlet, c) cooler and d) exhaust stack.

Air quality monitoring is a rapidly growing area of citizen science, or community science (CS), thanks to the availability of low-cost sensors. Contributing to a crowdsourced data platform (e.g., http:// purpleair .com/ map) is usually easy in urban areas, where there is access to uninterrupted electricity and wireless internet (Wi-Fi). However, there are sometimes security restrictions on Wi-Fi or a lack of exterior power outlets. Also, rural regions, particularly in low- and middle-income countries, often lack electricity and Wi-Fi continuity. RTI International has designed and distributed a solar power and Wi-Fi station that can adequately power both a small air quality sensor (e.g., PurpleAir PA-II) and a Wi-Fi hotspot to overcome these challenges. The station housing can accommodate a battery, a controller, and a cell phone or another type of Wi-Fi hotspot device. This paper discusses the need for such a station; a design for the current station, including parts list; suggestions for modifications in various use cases; and design factors to consider, including amount of sunlight per day, intended number of operational days under cloudy conditions, season, and total power requirements. This method is intended to be open source and a starting point for citizen scientists and CS projects.