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The smart grid is widely considered to be the informationization of the power grid. As an essential characteristic of the smart grid, demand response can reschedule the users' energy consumption to reduce the operating expense from expensive generators and further to defer the capacity addition in the long run. This survey comprehensively explores the four major aspects -programs, issues, approaches, and future extensions -of demand response. Specifically, we first introduce the means/tariffs that the power utility takes to incentivize users to reschedule their energy usage patterns. Then we survey the existing mathematical models and problems in the previous and current literatures, followed by the state-of-the-art approaches and solutions to address these issues. Finally, based on the above overview, we also outline the potential challenges and future research directions in the context of demand response.

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Economics and environmental incentives, as well as advances in technology, are reshaping the traditional view of industrial systems. The anticipation of a large penetration of plug-in hybrid electric vehicles (PHEVs) and plug-in electric vehicles (PEVs) into the market brings up many technical problems that are highly related to industrial information technologies within the next ten years. There is a need for an in-depth understanding of the electrification of transportation in the industrial environment. It is important to consolidate the practical and the conceptual knowledge of industrial informatics in order to support the emerging electric vehicle (EV) technologies. This paper presents a comprehensive overview of the electrification of transportation in an industrial environment. In addition, it provides a comprehensive survey of the EVs in the field of industrial informatics systems, namely: 1) charging infrastructure and PHEV/PEV batteries; 2) intelligent energy management; 3) vehicle-to-grid; and 4) communication requirements. Moreover, this paper presents a future perspective of industrial information technologies to accelerate the market introduction and penetration of advanced electric drive vehicles.Index Terms-Battery, charging infrastructure, communication, electric vehicle (EV), energy management, plug-in electric vehicle (PEV), plug-in hybrid electric vehicle (PHEV), smart grid, vehicle-to-grid (V2G).

Motor systems are very important in modern society. They convert almost 60% of the electricity produced in the U.S. into other forms of energy to provide power to other equipment. In the performance of all motor systems, bearings play an important role. Many problems arising in motor operations are linked to bearing faults. In many cases, the accuracy of the instruments and devices used to monitor and control the motor system is highly dependent on the dynamic performance of the motor bearings. Thus, fault diagnosis of a motor system is inseparably related to the diagnosis of the bearing assembly. In this paper, bearing vibration frequency features are discussed for motor bearing fault diagnosis. This paper then presents an approach for motor rolling bearing fault diagnosis using neural networks and time/frequency-domain bearing vibration analysis. Vibration simulation is used to assist in the design of various motor rolling bearing fault diagnosis strategies. Both simulation and real-world testing results obtained indicate that neural networks can be effective agents in the diagnosis of various motor bearing faults through the measurement and interpretation of motor bearing vibration signatures.

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