Rajit Gadh

Collaborative virtual prototyping of product assemblies over the Internet

Product assembly design is a complex activity involving collaboration between several designers. Currently, many manufacturing firms out source the design and development of different portions of a product to different suppliers. Due to the absence of an integrated tool for collaborative product assembly design, designers currently follow an indirect approach. This makes collaboration an unorganized and inefficient activity. This paper discusses the architectural novelties and design considerations utilized to develop an integrated collaborative assembly design tool. Further, the scenarios of using such a tool are discussed in detail. This discussion highlights how the tool contributes towards making collaboration an organized and efficient activity and enables designers to rapidly design and prototype the product assembly.

Distributed Optimal Energy Management in Microgrids

Energy management in microgrids is typically formulated as a nonlinear optimization problem. Solving it in a centralized manner does not only require high computational capabilities at the microgrid central controller (MGCC), but may also infringe customer privacy. Existing distributed approaches, on the other hand, assume that all generations and loads are connected to one bus, and ignore the underlying power distribution network and the associated power flow and system operational constraints. Consequently, the schedules produced by those algorithms may violate those constraints and thus are not feasible in practice. Therefore, the focus of this paper is on the design of a distributed energy management strategy (EMS) for the optimal operation of microgrids with consideration of the distribution network and the associated constraints. Specifically, we formulate microgrid energy management as an optimal power flow problem, and propose a distributed EMS where the MGCC and the local controllers jointly compute an optimal schedule. We also provide an implementation of the proposed distributed EMS based on IEC 61850. As one demonstration, we apply the proposed distributed EMS to a real microgrid in Guangdong Province, China, consisting of photovoltaics, wind turbines, diesel generators, and a battery energy storage system. The simulation results demonstrate the effectiveness and fast convergence of the proposed distributed EMS.

APPLICATIONS OF RFID TECHNOLOGY AND SMART PARTS IN MANUFACTURING

Industrial and consumer applications of Radio Frequency Identification (RFID) are explored. The state-of-the-art and development in RFID technology is reviewed. A currently operative smart parts based manufacturing system is described which uses RFID as the key technology. The role of RFID in the emerging Wireless Internet Manufacturing field is highlighted.Copyright

Optimal Residential Demand Response in Distribution Networks

Demand response (DR) enables customers to adjust their electricity usage to balance supply and demand. Most previous works on DR consider the supply-demand matching in an abstract way without taking into account the underlying power distribution network and the associated power flow and system operational constraints. As a result, the schemes proposed by those works may end up with electricity consumption/shedding decisions that violate those constraints and thus are not feasible. In this paper, we study residential DR with consideration of the power distribution network and the associated constraints. We formulate residential DR as an optimal power flow problem and propose a distributed scheme where the load service entity and the households interactively communicate to compute an optimal demand schedule. To complement our theoretical results, we also simulate an IEEE test distribution system. The simulation results demonstrate two interesting effects of DR. One is the location effect, meaning that the households far away from the feeder tend to reduce more demands in DR. The other is the rebound effect, meaning that DR may create a new peak after the DR event ends if the DR parameters are not chosen carefully. The two effects suggest certain rules we should follow when designing a DR program.

Solar generation prediction using the ARMA model in a laboratory-level micro-grid

The goal of this article is to investigate and research solar generation forecasting in a laboratory-level micro-grid, using the UCLA Smart Grid Energy Research Center (SMERC) as the test platform. The article presents an overview of the existing solar forecasting models and provides an evaluation of various solar forecasting providers. The auto-regressive moving average (ARMA) model and the persistence model are used to predict the future solar generation within the vicinity of UCLA. In the forecasting procedures, the historical solar radiation data originates from SolarAnywhere. System Advisor Model (SAM) is applied to obtain the historical solar generation data, with inputting the data from SolarAnywhere. In order to validate the solar forecasting models, simulations in the System Identification Toolbox, Matlab platform are performed. The forecasting results with error analysis indicate that the ARMA model excels at short and medium term solar forecasting, whereas the persistence model performs well only under very short duration.

Recognition of geometric forms using the differential depth filter

Abstract The analysis of designs in single-piece part manufacturing during the design process results in significant benefits during the manufacturing stages. If manufacturability knowledge can be incorporated into the design, significant downstream problems may be eliminated. Manufacturing knowledge is often related to critiques on shape features that are special forms on parts that perform certain functions. The shape features required to analyze manufacturability are not always explicitly available in surface CAD models of parts, and they may need to be extracted. However, it is the variation of shape features with topology and geometry that makes the problem of feature recognition difficult. The research described in the paper focuses on shape-feature recognition that is based on the differential depth filter, which reduces the number of topological entities. A second level of operation causes the topological entities to be transformed into entities of a higher abstraction level called loops. Loops assist in reducing the number of entities in which features need to be searched for, which implies a reduction in the search space. Considerable success in feature recognition has been achieved for parts with varying topology and geometry and for parts with a relatively large number of topological entities (up to 30 000 faces and 40 000 edges).

Real-Time Energy Management in Microgrids

Energy management in microgrids is typically formulated as an offline optimization problem for day-ahead scheduling by previous studies. Most of these offline approaches assume perfect forecasting of the renewables, the demands, and the market, which is difficult to achieve in practice. Existing online algorithms, on the other hand, oversimplify the microgrid model by only considering the aggregate supply-demand balance while omitting the underlying power distribution network and the associated power flow and system operational constraints. Consequently, such approaches may result in control decisions that violate the real-world constraints. This paper focuses on developing an online energy management strategy (EMS) for real-time operation of microgrids that takes into account the power flow and system operational constraints on a distribution network. We model the online energy management as a stochastic optimal power flow problem and propose an online EMS based on Lyapunov optimization. The proposed online EMS is subsequently applied to a real-microgrid system. The simulation results demonstrate that the performance of the proposed EMS exceeds a greedy algorithm and is close to an optimal offline algorithm. Lastly, the effect of the underlying network structure on energy management is observed and analyzed.

Planetary-Scale RFID Services in an Age of Uberveillance

Radio-frequency identification (RFID) has a great number of unfulfilled prospects. Part of the problem until now has been the value proposition behind the technology-it has been marketed as a replacement technique for the barcode when the reality is that it has far greater capability than simply non-line-of-sight identification, towards decision making in strategic management and reengineered business processes. The vision of the internet of things (IOT) has not eventuated but a world in which every object you can see around you carries the possibility of being connected to the internet is still within the realm of possibility. However incremental innovations may see RFID being sold as a service (much like photocopiers are maintained today) than a suite of technologies within a system that are sold as individual or bundled packaged components. This paper outlines the vision for such a product service system, what kinds of smart applications we are likely to see in the future as a result, and the importance of data management capabilities in planetary-scale systems.

Optimal sizing and placement of battery energy storage in distribution system based on solar size for voltage regulation

This paper proposes a new strategy to achieve voltage regulation in distributed power systems in the presence of solar energy sources and battery storage systems. The goal is to find the minimum size of battery storage and its corresponding location in the network based on the size and place of the integrated solar generation. The proposed method formulates the problem by employing the network impedance matrix to obtain an analytical solution instead of using a recursive algorithm such as power flow. The required modifications for modeling the slack and PV buses (generator buses) are utilized to increase the accuracy of the approach. The use of reactive power control to regulate the voltage regulation is not always an optimal solution as in distribution systems R/X is large. In this paper the minimum size and the best place of battery storage is achieved by optimizing the amount of both active and reactive power exchanged by battery storage and its grid-tie inverter (GTI) based on the network topology and R/X ratios in the distribution system. Simulation results for the IEEE 14-bus system verify the effectiveness of the proposed approach.

Fast Prediction for Sparse Time Series: Demand Forecast of EV Charging Stations for Cell Phone Applications

This paper proposes a new cellphone application algorithm which has been implemented for the prediction of energy consumption at electric vehicle (EV) charging stations at the University of California, Los Angeles (UCLA). For this interactive user application, the total time for accessing the database, processing the data, and making the prediction needs to be within a few seconds. We first analyze three relatively fast machine learning-based time series prediction algorithms and find that the nearest neighbor (NN) algorithm (k NN with k = 1) shows better accuracy. Considering the sparseness of the time series of the charging records, we then discuss the new algorithm based on the new proposed time-weighted dot product (TWDP) dissimilarity measure to improve the accuracy and processing time. Two applications have been designed on top of the proposed prediction algorithm: one predicts the expected available energy at the outlet and the other one predicts the expected charging finishing time. The total time, including accessing the database, data processing, and prediction is approximately 1 s for both applications. The granularity of the prediction is 1 h and the horizon is 24 h; data have been collected from 20 EV charging outlets.