Connectivity analysis in clustered wireless sensor networks powered by solar energy

©2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Emerging 5G communication paradigms, such as machine-type communication, have triggered an explosion in ad-hoc applications that require connectivity among the nodes of wireless networks. Ensuring a reliable network operation under fading conditions is not straightforward, as the transmission schemes and the network topology, i.e., uniform or clustered deployments, affect the performance and should be taken into account. Moreover, as the number of nodes increases, exploiting natural energy sources and wireless energy harvesting (WEH) could be the key to the elimination of maintenance costs while also boosting immensely the network lifetime. In this way, zero-energy wireless-powered sensor networks (WPSNs) could be achieved, if all components are powered by green sources. Hence, designing accurate mathematical models that capture the network behavior under these circumstances is necessary to provide a deeper comprehension of such networks. In this paper, we provide an analytical model for the connectivity in a large-scale zero-energy clustered WPSN under two common transmission schemes, namely, unicast and broadcast. The sensors are WEH-enabled, while the network components are solar-powered and employ a novel energy allocation algorithm. In our results, we evaluate the tradeoffs among the various scenarios via extensive simulations and identify the conditions that yield a fully connected zero-energy WPSN.Peer ReviewedPostprint (author's final draft

An Integrated Multi-Time-Scale Modeling for Solar Irradiance Forecasting Using Deep Learning

For short-term solar irradiance forecasting, the traditional point forecasting methods are rendered less useful due to the non-stationary characteristic of solar power. The amount of operating reserves required to maintain reliable operation of the electric grid rises due to the variability of solar energy. The higher the uncertainty in the generation, the greater the operating-reserve requirements, which translates to an increased cost of operation. In this research work, we propose a unified architecture for multi-time-scale predictions for intra-day solar irradiance forecasting using recurrent neural networks (RNN) and long-short-term memory networks (LSTMs). This paper also lays out a framework for extending this modeling approach to intra-hour forecasting horizons thus, making it a multi-time-horizon forecasting approach, capable of predicting intra-hour as well as intra-day solar irradiance. We develop an end-to-end pipeline to effectuate the proposed architecture. The performance of the prediction model is tested and validated by the methodical implementation. The robustness of the approach is demonstrated with case studies conducted for geographically scattered sites across the United States. The predictions demonstrate that our proposed unified architecture-based approach is effective for multi-time-scale solar forecasts and achieves a lower root-mean-square prediction error when benchmarked against the best-performing methods documented in the literature that use separate models for each time-scale during the day. Our proposed method results in a 71.5% reduction in the mean RMSE averaged across all the test sites compared to the ML-based best-performing method reported in the literature. Additionally, the proposed method enables multi-time-horizon forecasts with real-time inputs, which have a significant potential for practical industry applications in the evolving grid.Comment: 19 pages, 12 figures, 3 tables, under review for journal submissio

Hydrogen vs. Battery in the long-term operation. A comparative between energy management strategies for hybrid renewable microgrids

The growth of the world’s energy demand over recent decades in relation to energy intensity and demography is clear. At the same time, the use of renewable energy sources is pursued to address decarbonization targets, but the stochasticity of renewable energy systems produces an increasing need for management systems to supply such energy volume while guaranteeing, at the same time, the security and reliability of the microgrids. Locally distributed energy storage systems (ESS) may provide the capacity to temporarily decouple production and demand. In this sense, the most implemented ESS in local energy districts are small–medium-scale electrochemical batteries. However, hydrogen systems are viable for storing larger energy quantities thanks to its intrinsic high mass-energy density. To match generation, demand and storage, energy management systems (EMSs) become crucial. This paper compares two strategies for an energy management system based on hydrogen-priority vs. battery-priority for the operation of a hybrid renewable microgrid. The overall performance of the two mentioned strategies is compared in the long-term operation via a set of evaluation parameters defined by the unmet load, storage efficiency, operating hours and cumulative energy. The results show that the hydrogen-priority strategy allows the microgrid to be led towards island operation because it saves a higher amount of energy, while the battery-priority strategy reduces the energy efficiency in the storage round trip. The main contribution of this work lies in the demonstration that conventional EMS for microgrids’ operation based on battery-priority strategy should turn into hydrogen-priority to keep the reliability and independence of the microgrid in the long-term operation

Strict Upper Limits on the Carbon-to-Oxygen Ratios of Eight Hot Jupiters from Self-Consistent Atmospheric Retrieval

The elemental compositions of hot Jupiters are informative relics of planet formation that can help us answer long-standing questions regarding the origin and formation of giant planets. Here, I present the main conclusions from a comprehensive atmospheric retrieval survey of eight hot Jupiters with detectable molecular absorption in their near-infrared transmission spectra. I analyze the eight transmission spectra using the newly-developed, self-consistent atmospheric retrieval framework, SCARLET. Unlike previous methods, SCARLET combines the physical and chemical consistency of complex atmospheric models with the statistical treatment of observational uncertainties known from atmospheric retrieval techniques. I find that all eight hot Jupiters consistently require carbon-to-oxygen ratios (C/O) below 0.9. The finding of C/O<0.9 is highly robust for HD209458b, WASP-12b, WASP-19b, HAT-P-1b, and XO-1b. For HD189733b, WASP-17b, and WASP-43b, I find that the published WFC3 transmission spectra favor C/O<0.9 at greater than 95% confidence. I further show that the water abundances on all eight hot Jupiters are consistent with solar composition. The relatively small depth of the detected water absorption features is due to the presence of clouds, not due to a low water abundance as previously suggested for HD209458b. The presence of a thick cloud deck is inferred for HD209458b and WASP-12b. HD189733b may host a similar cloud deck, rather than the previously suggested Rayleigh hazes, if star spots affect the observed spectrum. The approach taken in SCARLET can be regarded as a new pathway to interpreting spectral observations of planetary atmospheres. In this work, including our prior knowledge of H-C-N-O chemistry enables me to constrain the C/O ratio without detecting a single carbon-bearing molecule.Comment: under review at ApJ; updated to account for recently announced observations of WASP-12b and HD 209458

Analyzing tree distribution and abundance in Yukon-Charley Rivers National Preserve: developing geostatistical Bayesian models with count data

Master's Project (M.S.) University of Alaska Fairbanks, 2018Species distribution models (SDMs) describe the relationship between where a species occurs and underlying environmental conditions. For this project, I created SDMs for the five tree species that occur in Yukon-Charley Rivers National Preserve (YUCH) in order to gain insight into which environmental covariates are important for each species, and what effect each environmental condition has on that species' expected occurrence or abundance. I discuss some of the issues involved in creating SDMs, including whether or not to incorporate spatially explicit error terms, and if so, how to do so with generalized linear models (GLMs, which have discrete responses). I ran a total of 10 distinct geostatistical SDMs using Markov Chain Monte Carlo (Bayesian methods), and discuss the results here. I also compare these results from YUCH with results from a similar analysis conducted in Denali National Park and Preserve (DNPP)

Modeling Non-Stationary Processes Through Dimension Expansion

In this paper, we propose a novel approach to modeling nonstationary spatial fields. The proposed method works by expanding the geographic plane over which these processes evolve into higher dimensional spaces, transforming and clarifying complex patterns in the physical plane. By combining aspects of multi-dimensional scaling, group lasso, and latent variables models, a dimensionally sparse projection is found in which the originally nonstationary field exhibits stationarity. Following a comparison with existing methods in a simulated environment, dimension expansion is studied on a classic test-bed data set historically used to study nonstationary models. Following this, we explore the use of dimension expansion in modeling air pollution in the United Kingdom, a process known to be strongly influenced by rural/urban effects, amongst others, which gives rise to a nonstationary field