skip to main content

Title: Design Resilience of Demand Response Systems Utilizing Locally Communicating Thermostatically Controlled Loads

Thermostatically Controlled Loads (TCLs) have shown great potential for Demand Response (DR) events. The focus of this study is to investigate the effects of adding communication throughout a population of TCLs on the resilience of the system. A Metric for resilience is calculated on varying populations of TCLs and verified with agent based modeling simulations. At the core of this study is an added thermostat criterion created from the combination of a proportional gain and the average compressor operating state of neighboring TCLs. Differing connection architectures are also analyzed. Resilience of the systems under different connection topologies, are calculated by analyzing algebraic connectivity at varying population sizes. The resilience analysis was verified through simulation. Results of the analysis show the effect of on delay schemes and connection architecture on stability limit of each system. Good concurrence was found between predicted and observed resilience for smaller dead-band sizes. Simulations showed varying results on the effect of a simulated attack based on location of the attack within the population.

; ;
Award ID(s):
Publication Date:
Journal Name:
ASME 2019 International Mechanical Engineering Congress and Exposition
Sponsoring Org:
National Science Foundation
More Like this
  1. Abstract
    Excessive phosphorus (P) applications to croplands can contribute to eutrophication of surface waters through surface runoff and subsurface (leaching) losses. We analyzed leaching losses of total dissolved P (TDP) from no-till corn, hybrid poplar (Populus nigra X P. maximowiczii), switchgrass (Panicum virgatum), miscanthus (Miscanthus giganteus), native grasses, and restored prairie, all planted in 2008 on former cropland in Michigan, USA. All crops except corn (13 kg P ha−1 year−1) were grown without P fertilization. Biomass was harvested at the end of each growing season except for poplar. Soil water at 1.2 m depth was sampled weekly to biweekly for TDP determination during March–November 2009–2016 using tension lysimeters. Soil test P (0–25 cm depth) was measured every autumn. Soil water TDP concentrations were usually below levels where eutrophication of surface waters is frequently observed (> 0.02 mg L−1) but often higher than in deep groundwater or nearby streams and lakes. Rates of P leaching, estimated from measured concentrations and modeled drainage, did not differ statistically among cropping systems across years; 7-year cropping system means ranged from 0.035 to 0.072 kg P ha−1 year−1 with large interannual variation. Leached P was positively related to STP, which decreased over the 7 years in all systems. These results indicate that both P-fertilized and unfertilized cropping systems mayMore>>
  2. Abstract

    Geological storage of carbon dioxide (CO2) in depleted gas reservoirs represents a cost-effective solution to mitigate global carbon emissions. The surface chemistry of the reservoir rock, pressure, temperature, and moisture content are critical factors that determine the CO2 adsorption capacity and storage mechanisms. Shale-gas reservoirs are good candidates for this application. However, the interactions of CO2 and organic content still need further investigation. The objectives of this paper are to (i) experimentally investigate the effect of pressure and temperature on the CO2 adsorption capacity of activated carbon, (ii) quantify the nanoscale interfacial interactions between CO2 and the activated carbon surface using Monte Carlo molecular modeling, and (iii) quantify the correlation between the adsorption isotherms of activated carbon-CO2 system and the actual carbon dioxide adsorption on shale-gas rock at different temperatures and geochemical conditions. Activated carbon is used as a proxy for kerogen. The objectives aim at obtaining a better understanding of the behavior of CO2 injection and storage into shale-gas formations.

    We performed experimental measurements and Grand Canonical Monte Carlo (GCMC) simulations of CO2 adsorption onto activated carbon. The experimental work involved measurements of the high-pressure adsorption capacity of activated carbon using pure CO2 gas. Subsequently, we performed amore »series of GCMC simulations to calculate CO2 adsorption capacity on activated carbon to validate the experimental results. The simulated activated carbon structure consists of graphite sheets with a distance between the sheets equal to the average actual pore size of the activated carbon sample. Adsorption isotherms were calculated and modeled for each temperature value at various pressures.

    The adsorption of CO2 on activated carbon is favorable from the energy and kinetic point of view. This is due to the presence of a wide micro to meso pore sizes that can accommodate a large amount of CO2 particles. The results of the experimental work show that excess adsorption results for gas mixtures lie in between the results for pure components. The simulation results agree with the experimental measurements. The strength of CO2 adsorption depends on both surface chemistry and pore size of activated carbon. Once strong adsorption sites within nanoscale network are established, gas adsorption even at very low pressure is governed by pore width rather than chemical composition. The outcomes of this paper provides new insights about the parameters affecting CO2 adsorption and storage in shale-gas reservoirs, which is critical for developing standalone representative models for CO2 adsorption on pure organic carbon.

    « less

    We present the discovery of 37 pulsars from ∼ 20 yr old archival data of the Parkes Multibeam Pulsar Survey using a new FFT-based search pipeline optimized for discovering narrow-duty cycle pulsars. When developing our pulsar search pipeline, we noticed that the signal-to-noise ratios of folded and optimized pulsars often exceeded that achieved in the spectral domain by a factor of two or greater, in particular for narrow duty cycle ones. Based on simulations, we verified that this is a feature of search codes that sum harmonics incoherently and found that many promising pulsar candidates are revealed when hundreds of candidates per beam even with modest spectral signal-to-noise ratios of S/N∼5–6 in higher-harmonic folds (up to 32 harmonics) are folded. Of these candidates, 37 were confirmed as new pulsars and a further 37 would have been new discoveries if our search strategies had been used at the time of their initial analysis. While 19 of these newly discovered pulsars have also been independently discovered in more recent pulsar surveys, 18 are exclusive to only the Parkes Multibeam Pulsar Survey data. Some of the notable discoveries include: PSRs J1635−47 and J1739−31, which show pronounced high-frequency emission; PSRs J1655−40 and J1843−08 belongmore »to the nulling/intermittent class of pulsars; and PSR J1636−51 is an interesting binary system in a ∼0.75 d orbit and shows hints of eclipsing behaviour – unusual given the 340 ms rotation period of the pulsar. Our results highlight the importance of reprocessing archival pulsar surveys and using refined search techniques to increase the normal pulsar population.

    « less
  4. Abstract Motivation

    Network-based analyses of high-throughput genomics data provide a holistic, systems-level understanding of various biological mechanisms for a common population. However, when estimating multiple networks across heterogeneous sub-populations, varying sample sizes pose a challenge in the estimation and inference, as network differences may be driven by differences in power. We are particularly interested in addressing this challenge in the context of proteomic networks for related cancers, as the number of subjects available for rare cancer (sub-)types is often limited.


    We develop NExUS (Network Estimation across Unequal Sample sizes), a Bayesian method that enables joint learning of multiple networks while avoiding artefactual relationship between sample size and network sparsity. We demonstrate through simulations that NExUS outperforms existing network estimation methods in this context, and apply it to learn network similarity and shared pathway activity for groups of cancers with related origins represented in The Cancer Genome Atlas (TCGA) proteomic data.

    Availability and implementation

    The NExUS source code is freely available for download at

    Supplementary information

    Supplementary data are available at Bioinformatics online.

  5. Abstract Background

    The topology of metabolic networks is both well-studied and remarkably well-conserved across many species. The regulation of these networks, however, is much more poorly characterized, though it is known to be divergent across organisms—two characteristics that make it difficult to model metabolic networks accurately. While many computational methods have been built to unravel transcriptional regulation, there have been few approaches developed for systems-scale analysis and study of metabolic regulation. Here, we present a stepwise machine learning framework that applies established algorithms to identify regulatory interactions in metabolic systems based on metabolic data: stepwise classification of unknown regulation, or SCOUR.


    We evaluated our framework on both noiseless and noisy data, using several models of varying sizes and topologies to show that our approach is generalizable. We found that, when testing on data under the most realistic conditions (low sampling frequency and high noise), SCOUR could identify reaction fluxes controlled only by the concentration of a single metabolite (its primary substrate) with high accuracy. The positive predictive value (PPV) for identifying reactions controlled by the concentration of two metabolites ranged from 32 to 88% for noiseless data, 9.2 to 49% for either low sampling frequency/low noise or high sampling frequency/high noisemore »data, and 6.6–27% for low sampling frequency/high noise data, with results typically sufficiently high for lab validation to be a practical endeavor. While the PPVs for reactions controlled by three metabolites were lower, they were still in most cases significantly better than random classification.


    SCOUR uses a novel approach to synthetically generate the training data needed to identify regulators of reaction fluxes in a given metabolic system, enabling metabolomics and fluxomics data to be leveraged for regulatory structure inference. By identifying and triaging the most likely candidate regulatory interactions, SCOUR can drastically reduce the amount of time needed to identify and experimentally validate metabolic regulatory interactions. As high-throughput experimental methods for testing these interactions are further developed, SCOUR will provide critical impact in the development of predictive metabolic models in new organisms and pathways.

    « less