Intermittent Availability Research Articles

Power Electronic Converter Configurations Integration with Hybrid Energy Sources – A Comprehensive Review for State-of the-Art in Research

The electricity demand is increasing day by day. On the other side, fossil fuels are depleting at a higher rate. The abundantly available renewable energy sources like solar, wind, and fuel cells are becoming popular sources of energy. But due to the intermittent availability of these renewable energy sources, two or more energy sources are integrated together. Power electronic converters are used for integrating different energy sources. Various power electronic converter (PEC) topologies have been proposed and analyzed in detail to control maximum power point, voltage, frequency, and harmonic distortions. Each of these configurations will differ in its topology and operating principle. The objective of this paper is to present a comprehensive review of various aspects of PEC configurations available for integration of renewable energy sources by systematization into three groups (AC shunt coupled, DC shunt coupled, and hybrid coupled systems). Emphasis is also given to coordination power control, maximum power point, and grid integration challenges related to the hybrid energy system. Furthermore, a general, system modeling of solar, wind, and grid integration is presented here to give an overall picture of a hybrid renewable energy system.

Diagnosis and management of surgical disease at Ethiopian health centres: cross-sectional survey of resources and barriers to care

ObjectivesThe aim of this study was to characterise the resources and challenges for surgical care and referrals at health centres (HCs) in South Wollo Zone, Ethiopia.SettingEight primary HCs in South...

Real-Time Data Retrieval in Cyber-Physical Systems with Temporal Validity and Data Availability Constraints

Maintaining the temporal validity of real-time data in cyber-physical systems is of critical importance to ensure the correct decision making and appropriate system operation. Most existing work on real-time data retrieval assume that the real-time data under study are always available for retrieval, and the developed scheduling algorithms mainly focus on making real-time decisions while meeting the temporal validity constraints. This assumption, however does not hold in many real-time applications with intermittent data availability. In this paper, we study the Availability-constrained Fresh Data Retrieval (AFDR) problem, which aims to retrieve all required real-time data for a given set of decision tasks on time while taking both the temporal validity and data availability constraints into consideration. We formulate the AFDR problem as an ILP problem and study its complexity under different settings. Given the general case of the AFDR problem is proved to be NP-hard, we focus on the cases that data items have unit-size retrieval time. For the single decision task scenario, we propose a polynomial-time optimal data retrieval algorithm, which consists of a task finish time selection phase and an optimal retrieval schedule construction phase, to solve the AFDR problem. For the multiple decision task scenario, we propose an efficient heuristic algorithm by transforming the temporal validity constraint of a real-time data item to the availability constraint. The effectiveness of the proposed algorithms has been validated through extensive experiments. Our results show that the heuristic algorithm outputs around $1.5\times$1.5× feasible cases compared to that of the state-of-the-art scheme.

Experimental investigation on the melting and solidification behavior of erythritol in a horizontal shell and multi-finned tube latent heat storage unit

Renewable energy sources are incapable of supplying continuous power due to their intermittent availability. To tackle this, energy storage systems incorporating phase change materials can be utilized. Among the several techniques, shell-and-finned-tube configurations show promising heat transfer performance and have greater engineering applicability. This study experimentally investigated the heat transfer mechanism in a horizontal shell-and-multi-finned-tube energy storage unit. Variation of average temperature and storage effectiveness as well as effects of inlet heat transfer fluid temperature and flow rate on the phase change of erythritol were experimentally investigated. It was found that the effect of inlet temperature was more significant than the flow rate (12–32% and 7–17%, respectively). Moreover, natural convection was found to significantly influence the heat transfer during melting where the melted material initially occupied the upper region of the unit and then moved from top to bottom. The outcomes of the present work help understand the effect of natural convection to be effectively utilized for the design and optimization of such storage configurations.

Rewards interact with repetition-dependent learning to enhance long-term retention of motor memories.

The combination of behavioral experiences that enhance long-term retention remains largely unknown. Informed by neurophysiological lines of work, this study tested the hypothesis that performance-contingent monetary rewards potentiate repetition-dependent forms of learning, as induced by extensive practice at asymptote, to enhance long-term retention of motor memories. To this end, six groups of 14 participants (n=84) acquired novel motor behaviors by adapting to a gradual visuomotor rotation while these factors were manipulated. Retention was assessed 24 h later. While all groups similarly acquired the novel motor behaviors, results from the retention session revealed an interaction indicating that rewards enhanced long-term retention, but only when practice was extended to asymptote. Specifically, the interaction indicated that this effect selectively occurred when rewards were intermittently available (i.e., 50%), but not when they were absent (i.e., 0%) or continuously available (i.e., 100%) during acquisition. This suggests that the influence of rewards on extensive practice and long-term retention is nonlinear, as continuous rewards did not further enhance retention as compared with intermittent rewards. One possibility is that rewards' intermittent availability allows to maintain their subjective value during acquisition, which may be key to potentiate long-term retention.

The effect of O2 and pressure on thiosulfate oxidation by Thiomicrospira thermophila.

Microbial sulfur cycling in marine sediments often occurs in environments characterized by transient chemical gradients that affect both the availability of nutrients and the activity of microbes. High turnover rates of intermediate valence sulfur compounds and the intermittent availability of oxygen in these systems greatly impact the activity of sulfur-oxidizing micro-organisms in particular. In this study, the thiosulfate-oxidizing hydrothermal vent bacterium Thiomicrospira thermophila strain EPR85 was grown in continuous culture at a range of dissolved oxygen concentrations (0.04-1.9mM) and high pressure (5-10MPa) in medium buffered at pH 8. Thiosulfate oxidation under these conditions produced tetrathionate, sulfate, and elemental sulfur, in contrast to previous closed-system experiments at ambient pressure during which thiosulfate was quantitatively oxidized to sulfate. The maximum observed specific growth rate at 5MPa pressure under unlimited O2 was 0.25hr-1 . This is comparable to the μmax (0.28hr-1 ) observed at low pH (<6) at ambient pressure when T.thermophila produces the same mix of sulfur species. The half-saturation constant for O2 ( ) estimated from this study was 0.2mM (at a cell density of 105 cells/ml) and was robust at all pressures tested (0.4-10MPa), consistent with piezotolerant behavior of this strain. The cell-specific was determined to be 1.5 pmol O2 /cell. The concentrations of products formed were correlated with oxygen availability, with tetrathionate production in excess of sulfate production at all pressure conditions tested. This study provides evidence for transient sulfur storage during times when substrate concentration exceeds cell-specific and subsequent consumption when oxygen dropped below that threshold. These results may be common among sulfur oxidizers in a variety of environments (e.g., deep marine sediments to photosynthetic microbial mats).

An integrated scheme for streaming scalable encoded video-on-demand over CR networks

In this paper, we propose an adaptive scheme to deliver scalable VoD streams over a cognitive radio network (CRN) that consists of a base station (BS) and multiple secondary users (SUs). The proposed scheme aims at improving the perceptual quality of the received videos at the SUs end while maintaining a continuous playback. To do so, the scheme opportunistically exploits the idle primary channel(s) as sensed by the BS. Moreover, to reduce the impact of the intermittent availability nature of the CR channels, we propose a channel allocation algorithm that fairly allocates the idle channel(s) among the active SUs while jointly taking into consideration the status of the playback buffers at the SUs as well as the quality of the available channels. Additionally, the proposed scheme schedules the video frames on the allocated slots by exploiting the flexibility of the scalable video encoder as well as the adaption of the used modulation level based on the channel conditions to meet a predetermined target bit error rate (BER). Simulation results demonstrate the efficiency of the proposed scheme in exploiting the scalability feature of the transmitted video sequences to efficiently use the dynamic CR resources while achieving improved peak signal-to-noise ratio of the reconstructed video streams with uninterrupted playback.

A Bilateral Tradeoff Decision Model for Wind Power Utilization with Extensive Load Scheduling

In this paper, we present the extensive load scheduling problem with intermittent and uncertain wind power availability. A chance-constrained bilateral tradeoff decision model is established to solve the problem. Our model aims at maximizing the wind power utilization and minimizing the system operation cost simultaneously by means of responsive loads, which are precisely divided into shiftable loads and high-energy loads. The chance constraint is applied to restrict the system imbalance with a small probability. Then, a revised sample average approximation (SAA) algorithm is developed to transform the chance constraint into sample average reformulations. Furthermore, the multi-objective differential evolution (MODE) method combined with SAA is proposed to solve the problem. Experiments enabling an effectiveness analysis of the two kinds of responsive loads are performed on the power system in Yancheng. The research of parameters of MODE, the sensitivity of different risk levels and the influence of iteration numbers are discussed. Finally, computational results prove that the combination of shiftable loads and high-energy loads have a better effect than adopting shiftable loads and high-energy loads separately, and the proposed method is convergent and valid in solving the problem.

A Stochastic-Based Rate Control Approach for Video Streaming Over Cognitive Radio Networks

Recently, the problem of video streaming over cognitive radio networks has attracted the attention of the research community due to the multiple challenges facing it. These challenges stem from the nature of the video content itself, the stochastic nature of wireless links as well as the intermittent availability of the licensed primary channels. Motivated by the above, this paper follows a different approach to tackle such a problem. Specifically, the proposed approach relies on obtaining an exact closed-form expression for the statistical distribution of the total amount of data that can be transmitted over the available primary users’ channels for any arbitrary interval of time. The validity of the obtained expression is verified through a comparison with Monte Carlo simulations. We then use this expression to propose a simple low complexity stochastic-based rate control approach for video streaming over cognitive radio networks. Due to its low computational complexity, the proposed approach can be easily implemented by any base station. The performance of the proposed approach is evaluated using the SimEvents simulator through reporting the average buffer occupancy, the peak signal-to-noise ratio and the inter-starvation interval. These metrics were reported for different parameters settings.

Design and analysis of a combined floating photovoltaic system for electricity and hydrogen production

The current study deals with a potential solution for the replacement of fossil fuel based energy resources with a sustainable solar energy resource. Electrical energy demand of a small community is investigated where a floating photovoltaic system and integrated hydrogen production unit are employed. Data are taken from Mumcular Dam located in Aegean Region of Turkey. PvSyst software is used for the simulation purposes. Furthermore, the obtained results are analyzed in the HOMER Pro Software. Photovoltaic (PV) electricity provides the required load and excess electricity to be used in the electrolyzer and to produce hydrogen. Saving lands by preventing their usage in conventional PV farms, saving the water due to reducing evaporation, and compensating the intermittent availability of solar energy are among the obtained results of the study for the considered scenario. Stored hydrogen is used to compensate the electric load through generating electricity by fuel cell. Floating PV (FPV) system decreases the water evaporation of water resources due to 3010 m2 shading area. FPV and Hydrogen Systems provides %99.43 of the electricity demand without any grid connection or fossil fuel usage, where 60.30 MWh/year of 211.94 MWh/year produced electricity is consumed by electric load at $0.6124/kWh levelized cost of electricity (LCOE).

Implementation of optimal scheduling model for power flow system

This paper mainly deals with the development of an energy management model using a supervisory control and data acquisition (SCADA) system. A predictive controller is implemented above the centralised SCADA platform. The distribution networks have been focused by a monitor, control and maintain equipment in the substations to reduce the operating cost. This research proposes a new energy management model that enables a flexible and also efficient operation of various power plants. The distribution control centre (DCC) is being monitored and controlled by SCADA systems and the DCC has become an important energy efficient policy concept. Based on the numerical calculations and graphical representations the renewable energy sources in both configurations, is independent of the enduring or intermittent main energy resource availability, which can lead to effective production.

A P-graph model for the synthesis of tri-generation systems for multi-period operation

The progressive development of renewable energy technologies is attributed to the growing energy demand and the depletion of non-renewable resources. Renewable energy technologies are usually characterized by intermittent availability of resources, affecting the capability of energy systems to meet projected energy demands. Tri-generation, or the simultaneous generation of three energy types, becomes a potential solution wherein integration of renewable energy technologies is used to maximize resource efficiency. Synthesizing these systems will require the consideration of techno-economic data and the use of computer-aided techniques to facilitate optimization and design. Mixed-Integer Linear Programming is a commonly used technique for Process Network Synthesis, but its application to complex problems becomes problematic due to numerous alternatives and parameter variations. The Process graph framework offers a less laborious option with its unambiguous representation of process systems, utilizing three algorithms in generating all combinatorially feasible solutions that present the user with both optimal and near-optimal solutions. These become helpful in decision-making especially when factors such as those impossible to be captured mathematically need to be considered. This work presents a P-graph model developed for the synthesis and design of multi-period, biomass-fired, tri-generation systems with a case study for power generation in the Philippines to demonstrate its capabilities.

Determinación del Modelo Estocástico del Estado de Carga de Baterías para el cómputo de Flujo de Potencia Probabilístico de Microrredes

Este documento propone una novedosa metodología para la estimación probabilística del modelo estocástico del estado de carga (SOC por su nombre en inglés “State of Charge”) de los sistemas de almacenamiento de energía mediante baterías (BESS por su nombre en inglés “Battery Energy Storage Systems”). La estimación apropiada del SOC es uno de los parámetros más importantes en la planificación de la expansión y operación de las microrredes. Para ello, se estructura una herramienta computacional que enlaza los programas de DIgSILENT PowerFactory y Python. Este aplicativo permite, de forma probabilística, evaluar la operación de la microrred considerando la disponibilidad del recurso primario intermitente de las fuentes de energía renovables y la variabilidad de la demanda eléctrica. Como resultado se determinan los modelos estocásticos del SOC del BESS para cada período de tiempo. La metodología propuesta se aplica a una microrred de prueba que se conecta a la “Barra 6” del sistema de prueba WSCC de tres máquinas y nueve barras, obteniéndose resultados prometedores.

High-Temperature Co-Electrolysis: A Versatile Method to Sustainably Produce Tailored Syngas Compositions

High-temperature co-electrolysis of carbon dioxide and steam is a promising method to produce ‘white’ syngas by making use of renewable energy and carbon dioxide as sustainable feedstock. The technological key advantage is the possibility to tailor syngas compositions over a broad range. This paper presents a systematic investigation of the syngas tailoring process by establishing relationships between feed gas compositions and flow rates to the syngas ratio. A linear dependence between the H2O:CO2 ratio in the feed gas and the H2:CO ratio in the output gas was observed. Furthermore, the syngas ratio remains mostly invariant upon variations in electrochemical potential and fluctuating gas utilizations/flow rates during operation of a co-electrolysis cell. Most importantly, the co-electrolysis performance was demonstrated to operate at high current densities of up to 3.2 A·cm−2 over a broad range of feed gas compositions with faradaic efficiencies of nearly 100%. The possibility to operate co-electrolysis under transient load conditions renders this method particularly suitable in future scenarios of intermittent availability of renewables. The results described here illustrate the versatility of co-electrolysis, which can produce all relevant syngas compositions in a single-step process at constantly high performance.

Conserved principles of transcriptional networks controlling metabolic flexibility in archaea.

Gene regulation is intimately connected with metabolism, enabling the appropriate timing and tuning of biochemical pathways to substrate availability. In microorganisms, such as archaea and bacteria, transcription factors (TFs) often directly sense external cues such as nutrient substrates, metabolic intermediates, or redox status to regulate gene expression. Intense recent interest has characterized the functions of a large number of such regulatory TFs in archaea, which regulate a diverse array of unique archaeal metabolic capabilities. However, it remains unclear how the co-ordinated activity of the interconnected metabolic and transcription networks produces the dynamic flexibility so frequently observed in archaeal cells as they respond to energy limitation and intermittent substrate availability. In this review, we communicate the current state of the art regarding these archaeal networks and their dynamic properties. We compare the topology of these archaeal networks to those known for bacteria to highlight conserved and unique aspects. We present a new computational model for an exemplar archaeal network, aiming to lay the groundwork toward understanding general principles that unify the dynamic function of integrated metabolic-transcription networks across archaea and bacteria.

Genomic insights into the metabolism of 'Candidatus Defluviicoccus seviourii', a member of Defluviicoccus cluster III abundant in industrial activated sludge.

Filamentous cluster III Defluviicoccus (DF3) are known to proliferate and cause bulking issues in industrial wastewater treatment plants. Members of the genus Defluviicoccus are also known to exhibit the glycogen accumulating organism (GAO) phenotype, which is suggested to be detrimental to enhanced biological phosphorus removal (EBPR). Despite the reported negative impact members of the DF3 have on activated sludge wastewater treatment systems, limited research has focused on understanding the physiological traits that allow them to compete in these environments. In this study, a near complete genome of an abundant filamentous DF3 named 'Candidatus Defluviicoccus seviourii' was obtained from a full-scale sequencing batch reactor (SBR) treating winery wastewater. Annotation of the 'Ca. D. seviourii' genome revealed interesting metabolic features that help to understand the abundance of this microorganism in industrial wastewater treatment plants. Their potential for the storage of polyhydroxyalkanoates (PHA) is suggested to favour these organisms with the intermittent availability of carbon in these systems. An ability to fix nitrogen and take up urea may provide them with an additional advantage with the characteristically high carbon to nitrogen content of industrial waste. The genome and preliminary findings of this study provide a foundation for further research into these biotechnologically relevant organisms.

Leaf-level photosynthetic capacity dynamics in relation to soil and foliar nutrients along forest-savanna boundaries in Ghana and Brazil.

Forest-savanna boundaries extend across large parts of the tropics but the variability of photosynthetic capacity in relation to soil and foliar nutrients across these transition zones is poorly understood. For this reason, we compared photosynthetic capacity (maximum rate of carboxylation of Rubisco at 25 C° (Vcmax25), leaf mass, nitrogen (N), phosphorus (P) and potassium (K) per unit leaf area (LMA, Narea, Parea and Karea, respectively), in relation to respective soil nutrients from 89 species at seven sites along forest-savanna ecotones in Ghana and Brazil. Contrary to our expectations, edaphic conditions were not reflected in foliar nutrient concentrations but LMA was slightly higher in lower fertility soils. Overall, each vegetation type within the ecotones demonstrated idiosyncratic and generally weak relationships between Vcmax25 and Narea, Parea and Karea. Species varied significantly in their Vcmax25 ↔ Narea relationship due to reduced investment of total Narea in photosynthetic machinery with increasing LMA. We suggest that studied species in the forest-savanna ecotones do not maximize Vcmax25 per given total Narea due to adaptation to intermittent water availability. Our findings have implications for global modeling of Vcmax25 and forest-savanna ecotone productivity.

Realizing Edge Marketplaces: Challenges and Opportunities

The edge of the network has the potential to host services for supporting a variety of user applications, ranging in complexity from data preprocessing, image and video rendering, and interactive gaming, to embedded systems in autonomous cars and built environments. However, the computational and data resources over which such services are hosted, and the actors that interact with these services, have an intermittent availability and access profile, introducing significant risk for user applications that must rely on them. This article investigates the development of an edge marketplace, which is able to support multiple providers for offering services at the network edge, and to enable demand supply for influencing the operation of such a marketplace. Resilience, cost, and quality of service and experience will subsequently enable such a marketplace to adapt its services over time. This article also describes how distributed-ledger technologies (such as blockchains) provide a promising approach to support the operation of such a marketplace and regulate its behavior (such as the GDPR in Europe) and operation. Two application scenarios provide context for the discussion of how such a marketplace would function and be utilized in practice.

Real-Time Data Retrieval With Multiple Availability Intervals in CPS Under Freshness Constraints

Maintaining the temporal validity of real-time data in cyber-physical systems (CPSs) is of critical importance to ensure correct decision making and appropriate system operation. Most existing work on real-time data retrieval assumes that the real-time data under study are always available, and the developed scheduling algorithms mainly focus on making real-time decisions while meeting the temporal validity (freshness) constraints. This assumption, however does not hold in many real-life CPS applications with intermittent data availability, such as in energy harvesting-based sensing systems. In this paper, we study the multi-interval availability-constrained fresh data retrieval (MAFDR) problem, which aims to retrieve all required real-time data on time for a set of decision tasks while taking both the temporal validity and data availability constraints into consideration. We present the formulation of the MAFDR problem and study its complexity under different settings. For the scenario of single decision task with unit-size data retrieval time, we propose a polynomial-time optimal data retrieval algorithm, which comprises a task finish time selection phase and an optimal retrieval schedule construction phase. For the general scenario of multiple decision tasks with nonunit-size data retrieval time, we provide an integer linear programming formulation for the MAFDR problem and propose a fast heuristic algorithm based on max flow. The effectiveness of the proposed algorithms has been validated through extensive experiments by comparing to the optimal solution and the state-of-the-art approach.

Cost‐minimizing online algorithm for internet green data centers on multi‐source energy

SummaryHuge energy consumption of large‐scale cloud data centers damages environments with excessive carbon emission. More and more data center operators are seeking to reduce carbon footprint via various types of renewable energy. However, the intermittent availability of renewable energy sources makes it quite challenging to cooperate with dynamically arriving workload. Meanwhile, the different natures (eg, price and carbon emission) of multiple energy sources also bring more challenges to achieve an optimal trade‐off among carbon emission, power cost, and service level agreement (SLA). In this paper, we study the problem of reducing the long‐term energy cost for geo‐distributed cloud centers, where multiple sources of renewable energy are considered and SLA requirement and carbon budget are satisfied. To tackle the randomness of workload arrival, varying electricity price, and intermittent supply of renewable energy, we first formulate the cost minimization problem as a constraint stochastic optimization problem. Second, based on Lyapunov optimization technique, we propose an online control algorithm to solve it and provide the rigorous theory analysis to demonstrate its performance. By converting the long‐term optimization problem to a mixed integer linear programming problem in each time slot, we analyze its inherent structure and propose an efficient algorithm to solve it based on Brenner's method. Our proposed algorithm makes online decisions rely only on the current system state and achieve cost emission trade‐off. Finally, the effectiveness of our algorithm is evaluated by extensive simulations based on real‐world data traces.