Optimal Dispatch Controller For Fuel Cell Integrated Building

Buildings contribute to around 40% of the total energy consumption in the US. Improvements to building operation offer substantial economic benefits and emissions reductions. Opportunities arise as more renewable energy sources are integrated into the power grid, where the inherent flexibility that buildings can provide become valuable assets for grid services. Stationary fuel cells providing combined heat and power (CHP) add more flexibility to building operation, where both significant electrical and thermal loads need to be met. As the technology matures, improved fuel cell responsiveness allows for advanced dynamic applications to maximize their utility within the building system. The integration of fuel cells and battery energy storage systems (BESS) to buildings presents several challenges and opportunities for optimal management of resources. In this work, we develop an optimal dispatch controller for real-time management of a fuel cell-integrated building system. The objective is to minimize building operating costs and maximizing profits from participating in the power grid ancillary service markets, while maintaining occupant comfort. To achieve this objective, we develop a specifically tailored model predictive control (MPC) algorithm to schedule the operation of a fuel cell, a BESS, and building equipment in response to the time-of-use electricity tariff. The controller determines the optimal schedules over a 24-hour horizon according to weather and building load forecast. This optimal schedule is implemented for a 1-hour period. Measurements from the fuel cell-integrated building are collected and used to update the optimization for the next 24-hour period. This recursive update ensures that the algorithm is robust to forecast errors and model mismatch. The effectiveness of the proposed algorithm is demonstrated with a co-simulation where the building is represented as a high-fidelity model in the EnergyPlus building simulation program and the optimal control is implemented in Matlab. The proposed optimal dispatch controller provides a tool to manage the real-time operation of a fuel cell-integrated building. It also helps building operators and the fuel cell industry assess the potential benefits of integrating stationary fuel cells with buildings

Peer-to-Peer Communication Trade-Offs for Smart Grid Applications

Virtual topologies in peer-to-peer networks can reduce the traffic consumed by altering the logical connectivity of peers without altering the underlying network. However, such sparsely connected virtual topologies do not focus on the needs for smart grid applications, which is information dissemination throughout the network, and in turn degrade the performance of distributed control algorithms running on peer-to-peer networks. This paper provides a flexible solution for application developers to prototype and deploy different virtual topologies that balances these trade-offs. First, it introduces a configurable virtual communication topology framework, TopLinkMgr, which enables users to specify any chosen connectivity configuration and deploy peer-to-peer applications using it. Second, it proposes a novel fault-tolerant self-adaptive virtual topology management algorithm, Bounded Path Dissemination, that can ensure the dissemination of information to all peers within a specified threshold. Experiments show that the algorithm improves on convergence speed and accuracy over state-of-the-art methods and is also robust against node failures while consuming significantly less communication bandwidth.Comment: 10 pages, 6 figure

Multi-roles affiliation model for general user profiling

National Research Foundation (NRF) Singapore under International Research Centres in Singapore Funding Initiativ

Regression Models for Identifying Noise Sources in Magnetic Resonance Images

Stochastic noise, susceptibility artifacts, magnetic field and radiofrequency inhomogeneities, and other noise components in magnetic resonance images (MRIs) can introduce serious bias into any measurements made with those images. We formally introduce three regression models including a Rician regression model and two associated normal models to characterize stochastic noise in various magnetic resonance imaging modalities, including diffusion-weighted imaging (DWI) and functional MRI (fMRI). Estimation algorithms are introduced to maximize the likelihood function of the three regression models. We also develop a diagnostic procedure for systematically exploring MR images to identify noise components other than simple stochastic noise, and to detect discrepancies between the fitted regression models and MRI data. The diagnostic procedure includes goodness-of-fit statistics, measures of influence, and tools for graphical display. The goodness-of-fit statistics can assess the key assumptions of the three regression models, whereas measures of influence can isolate outliers caused by certain noise components, including motion artifacts. The tools for graphical display permit graphical visualization of the values for the goodness-of-fit statistic and influence measures. Finally, we conduct simulation studies to evaluate performance of these methods, and we analyze a real dataset to illustrate how our diagnostic procedure localizes subtle image artifacts by detecting intravoxel variability that is not captured by the regression models

Use of Big Data Technology for Network Classroom Teaching Quality Management

Quality management of network classroom teaching has always been an urgent problem to be solved. Big data technology handles massive amounts of data and provides new quality management methods and means for network classroom teaching. However, data integration and fusion is a complex task and existing methods may not be able to deal with data fragmentation effectively, because data is often distributed across different systems and platforms in the network teaching environment. Therefore, this research aimed to study the quality management of network classroom teaching based on big data technology. This study provided a framework diagram of teaching quality evaluation criteria and factors affecting the teaching quality in the big data environment, explained complex relationships and effects among the factors, and described teaching quality prediction problems. The dimensionality reduction method of Least Absolute Shrinkage and Selection Operator (LASSO) was used for comprehensive status data integration of factors affecting teaching quality. An unequal-interval grey Riccati-Bernoulli model was constructed to study the internal relationships between various variable factors and network classroom teaching quality. Then the execution process of the prediction model, detailed modeling steps and teaching quality management steps were provided. The experimental results verified that the constructed model was effective

Forecasting Collector Road Speeds Under High Percentage of Missing Data

Accurate road speed predictions can help drivers in smart route planning. Although the issue has been studied previously, most existing work focus on arterial roads only, where sensors are configured closely for collecting complete real-time data. For collector roads where sensors sparsly cover, however, speed predictions are often ignored. With GPS-equipped floating car signals being available nowadays, we aim at forecasting collector road speeds by utilizing these signals. The main challenge compared with arterial roads comes from the missing data. In a time slot of the real case, over 90% of collector roads cannot be covered by enough floating cars. Thus most traditional approaches for arterial roads, relying on complete historical data, cannot be employed directly. Aiming at solving this problem, we propose a multi-view road speed prediction framework. In the first view, temporal patterns are modeled by a layered hidden Markov model; and in the second view, spatial patterns are modeled by a collective matrix factorization model. The two models are learned and inferred simultaneously in a co-regularized manner. Experiments conducted in the Beijing road network, based on 10K taxi signals in 2 years, have demonstrated that the approach outperforms traditional approaches by 10% in MAE and RMSE

Temporal evolution of microstructures in aqueous CTAB/SOS and CTAB/HDBS solutions

Vesicles are formed spontaneously when aqueous solutions of cetyl trimethylammonium bromide (CTAB) and sodium octyl sulfate (SOS), as well as CTAB and dodecyl benzene sulfonic acid (HDBS), are mixed in well-defined ratios. Microstructures in the starting solutions are composition-dependent and, in these experiments, include spherical and rodlike mieelles as well as monomers. Starting from these initial morphologies, relaxation to the equilibrium vesicle state can take several hours to months in the CTAB/SOS system, but the transition occurs within minutes in the CTAB/HDBS system at the concentrations studied. In this paper, the temporal evolution of aggregate microstructures from a range of initial states was monitored using time-resolved turbidity, dynamic light scattering, and cryogenic transmission electron microscopy (cryo-TEM). For the CTAB/SOS system, the turbidity changes slowly over a period of 2h. The rate of growth of the aggregates, measured by dynamic light scattering, was found to be independent of the specific morphology of the initial aggregates and of the added NaBr concentration. The morphologies of intermediate-state aggregates were directly identified by cryo-TEM observations of solutions quenched at different times after mixing and confirmed to be wormlike micelles, disks, and vesicles. The model that emerged for the transitions is that the micelles grow to floppy, undulating disks. The competition between the edge and bending energies drives the transition to small vesicles at a critical disk size. These vesicles then grow to the final size distribution. Varying proportions of each of these aggregates exist at all time points. In contrast to the CTAB/SOS results, both turbidity and dynamic light scattering reveal that the transition to the final size is rapid in the CTAB/HDBS system. Within the time resolution of the cryo-TEM measurements, only vesicles, and no disks are observed. These observations indicate that the bilayer bending energy dominates in this system. The solubility difference between SOS and HDBS could also play a role in the observed difference in kinetics