Does Peak Shaving & Storage Integration Green the Grid? ​

https://scholarworks.seattleu.edu/lightning-oct2021/1005/thumbnail.jp

Energy Optimizations for Smart Buildings and Smart Grids

Modern buildings are heavy power consumers. For instance, of the total electricity consumed in the US, 75% is consumed in the residential and commercial buildings. This consumption is not evenly distributed over time. Typical consumption profile exhibits several peaks and troughs. The peakiness, in turn, dictates the electric grid\u27s generation, transmission and distribution costs, and also the associated carbon emissions. This thesis discusses challenges involved in achieving the sustainability goals in buildings and electric grids. It investigates building and grid energy footprint optimization techniques to achieve the following goals: 1) making buildings energy efficient, 2) cutting building\u27s electricity bills, 3) cutting utility\u27s costs in electricity generation and distribution, 4) reducing carbon footprints, and 5) making the aggregate electricity consumption profile grid-friendly. In this thesis, we first design SmartCap, a system to enable homes flatten their consumption/demand by scheduling background loads (such as A/Cs, refrigerator), without causing user discomfort and without direct user involvement. Demand flattening facilitates aggregate peak reduction, which in turn enables grids to 1) reduce carbon emissions, and 2) cut installation and operational costs. Our results demonstrate that SmartCap can decrease the average deviation from mean power by over 20% across all periods with high deviation, thereby flattening the peaky demand. Next, we present SmartCharge, an intelligent battery charging system that shifts a building\u27s electricity consumption to off-peak periods by storing low-cost energy for use during high-cost periods, without active user involvement. We show that SmartCharge can typically save 10-15% in bills and can reduce the grid-wide peak demand by up to 20%. We then extend SmartCharge to GreenCharge, which integrates on-site renewables in a building\u27s electricity consumption. Our experiments show that GreenCharge can cut user electricity bills up to 20%. After GreenCharge, we investigate the use of large-scale distributed energy storage at buildings throughout the grid to flatten grid demand, while 1) maintaining end-user incentives for storage adoption at grid-scale, and 2) ensuring grid stability. We design PeakCharge, an online peak-aware charging algorithm to optimize the use of energy storage in the presence of a peak demand surcharge. Empirical evaluations show that total storage capacity required by PeakCharge to flatten grid demand is within 18% of the capacity required by a centralized system. Finally, we examine the efficacy of employing different combinations of energy storage technologies at different levels of the grid’s distribution hierarchy to cut electric utility\u27s daily operational costs. We present an optimization framework for modeling the primary characteristics of various energy storage technologies and important tradeoffs in placing different storage technologies at different levels of the distribution hierarchy. We show that by employing hybrid storage technologies at multiple levels of the distribution hierarchy, utilities can reduce their daily operating costs due to distributing electricity by up to 12%

Ready-to-use post-Newtonian gravitational waveforms for binary black holes with non-precessing spins: An update

For black-hole binaries whose spins are (anti-) aligned with respect to the orbital angular momentum of the binary, we compute the frequency domain phasing coefficients including the quadratic-in-spin terms up to the third post-Newtonian (3PN) order, the cubic-in-spin terms at the leading order, 3.5PN, and the spin-orbit effects up to the 4PN order. In addition, we obtain the 2PN spin contributions to the amplitude of the frequency-domain gravitational waveforms for non-precessing binaries, using recently derived expressions for the time-domain polarization amplitudes of binaries with generic spins, complete at that accuracy level. These two results are updates to Arun et al. (2009) [1] for amplitude and Wade et al. (2013) [2] for phasing. They should be useful to construct banks of templates that model accurately non-precessing inspiraling binaries, for parameter estimation studies, and or constructing analytical template families that accounts for the inspiral-merger-ringdown phases of the binary.Comment: 8 pages, an additional file (readable in MATHEMATICA) containing all the key results included in the sourc

DECODE: Data-driven Energy Consumption Prediction leveraging Historical Data and Environmental Factors in Buildings

Energy prediction in buildings plays a crucial role in effective energy management. Precise predictions are essential for achieving optimal energy consumption and distribution within the grid. This paper introduces a Long Short-Term Memory (LSTM) model designed to forecast building energy consumption using historical energy data, occupancy patterns, and weather conditions. The LSTM model provides accurate short, medium, and long-term energy predictions for residential and commercial buildings compared to existing prediction models. We compare our LSTM model with established prediction methods, including linear regression, decision trees, and random forest. Encouragingly, the proposed LSTM model emerges as the superior performer across all metrics. It demonstrates exceptional prediction accuracy, boasting the highest R2 score of 0.97 and the most favorable mean absolute error (MAE) of 0.007. An additional advantage of our developed model is its capacity to achieve efficient energy consumption forecasts even when trained on a limited dataset. We address concerns about overfitting (variance) and underfitting (bias) through rigorous training and evaluation on real-world data. In summary, our research contributes to energy prediction by offering a robust LSTM model that outperforms alternative methods and operates with remarkable efficiency, generalizability, and reliability.Comment: 11 pages, 6 figures, 6 table

Charged Particle and Photon Multiplicity, and Transverse Energy Production in High-Energy Heavy-Ion Collisions

We review the charged particle and photon multiplicity, and transverse energy production in heavy-ion collisions starting from few GeV to TeV energies. The experimental results of pseudorapidity distribution of charged particles and photons at different collision energies and centralities are discussed. We also discuss the hypothesis of limiting fragmentation and expansion dynamics using the Landau hydrodynamics and the underlying physics. Meanwhile, we present the estimation of initial energy density multiplied with formation time as a function of different collision energies and centralities. In the end, the transverse energy per charged particle in connection with the chemical freeze-out criteria is discussed. We invoke various models and phenomenological arguments to interpret and characterize the fireball created in heavy-ion collisions. This review overall provides a scope to understand the heavy-ion collision data and a possible formation of a deconfined phase of partons via the global observables like charged particles, photons and the transverse energy measurement.Comment: 27 pages, 43 figures, Invited Review for Advances in High Energy physics for Special Issue on "Global properties in High Energy Collisions

Prediction of pregnancy in artificial reproductive techniques through evaluation of thickness, morphology and vascularity of endometrium

Background: Prediction of pregnancy in Artificial Reproductive Techniques through evaluation of thickness, morphology and vascularity of endometrium.Methods: Endometrial thickness, morphology and sub endometrial blood flow were assessed using trans-vaginal ultrasound on the day of HCG in 200 undergoing IVF/ICSI treatment in the period between October 2009 and December 2014. Statistical analysis was done.Results: There was no difference in the demographic features between pregnant and non-pregnant women. Overall, 80 patients conceived; 46 (57.5%) of them had blood flow in zone III and 30 (37.5%) in zone II. All patients achieved pregnancy had endometrial thickness >8 mm. There was no significant difference in Doppler indices between pregnant and non-pregnant women.Conclusions: When the endometrial thickness is <8 mm, and if there are non-triple endometrial line, pregnancy rate decreases and the absence of colour mapping of the endometrium and subendometrial areas means and absolute implantation failure or a significant decrease of the implantation rate. Conversely, the pregnancy rate increases when the vessels reach endometrium

Abruptio placenta and its maternal and fetal outcome

Background: Abruptio placenta is one of the common cause of antepartum haemorrhage and is defined as premature separation of normally implanted placenta. It is more common in second half of pregnancy. Abruptio placenta is serious complication of pregnancy and causes high maternal and neonatal morbidity and mortality.Methods: This retrospective study of abruptio and its maternal and perinatal outcome was carried out between July 2016 and October 2017 at Rama Medical College Hospital and research centre.Results: Incidence of Abruptio placenta is 1.6%. It is most common in the women of age group 30-35 years. 75% of cases were associated with severe pre-eclampsia. Live births were 75% while stillbirths were 25%. PPH occurred in 30% of cases. DIC accounts for 25% of the complication.Conclusions: Abruptio placenta is life threatening complication of pregnancy and it is associated with poor maternal and fetal outcome if not managed appropriately. Hence early diagnosis and prompt resuscitative measures would prevent both perinatal and maternal mortality and morbidity