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Register a new userSmart Charging Technologies for Portable Electronic Devices
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Added by
Stefan Hild
Publication date
2012
fields
Informatics Engineering
and research's language is
English
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In this article we describe our efforts of extending demand-side control concepts to the application in portable electronic devices, such as laptop computers, mobile phones and tablet computers. As these devices feature built-in energy storage (in the form of batteries) and the ability to run complex control routines, they are ideal for the implementation of smart charging concepts. We developed a prototype of a smart laptop charger that controls the charging process depending on the locally measured frequency of the electricity grid. If this technique is incorporated into millions of devices in UK households, this will contribute significantly to the stability of the electricity grid, help to mitigate the power production fluctuations from renewable energy sources and avoid the high cost of building and maintaining conventional power plants as standby reserve.
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The past few years have seen an increasing focus on energy harvesting issue, including power supply for portable electric devices. Utilize scavenging ambient energy from the environment could eliminate the need for batteries and increase portable device lifetimes indefinitely. In addition, through MEMS technology fabricated micro-generator could easy integrate with these small or portable devices. Several different ambient sources, including solar, vibration and temperature effect, have already exploited [1-3]. Each energy source should be used in suitable environment, therefore to produce maximum efficiency. In this paper, we present an acoustic wave actuated micro-generator for power system by using the energy of acoustic waves, such as the sound from human voices or speakerphone, to actuate a MEMS-type electromagnetic transducer. This provides a longer device lifetime and greater power system convenience. Moreover, it is convenient to integrate MEMS-based microgenerators with small or porta le devices
As an environment-friendly substitute for conventional fuel-powered vehicles, electric vehicles (EVs) and their components have been widely developed and deployed worldwide. The large-scale integration of EVs into power grid brings both challenges and opportunities to the system performance. On one hand, the load demand from EV charging imposes large impact on the stability and efficiency of power grid. On the other hand, EVs could potentially act as mobile energy storage systems to improve the power network performance, such as load flattening, fast frequency control, and facilitating renewable energy integration. Evidently, uncontrolled EV charging could lead to inefficient power network operation or even security issues. This spurs enormous research interests in designing charging coordination mechanisms. A key design challenge here lies in the lack of complete knowledge of events that occur in the future. Indeed, the amount of knowledge of future events significantly impacts the design of efficient charging control algorithms. This article focuses on introducing online EV charging scheduling techniques that deal with different degrees of uncertainty and randomness of future knowledge. Besides, we highlight the promising future research directions for EV charging control.
We introduce and analyze Markov Decision Process (MDP) machines to model individual devices which are expected to participate in future demand-response markets on distribution grids. We differentiate devices into the following four types: (a) optional loads that can be shed, e.g. light dimming; (b) deferrable loads that can be delayed, e.g. dishwashers; (c) controllable loads with inertia, e.g. thermostatically-controlled loads, whose task is to maintain an auxiliary characteristic (temperature) within pre-defined margins; and (d) storage devices that can alternate between charging and generating. Our analysis of the devices seeks to find their optimal price-taking control strategy under a given stochastic model of the distribution market.
Wireless technologies can support a broad range of smart grid applications including advanced metering infrastructure (AMI) and demand response (DR). However, there are many formidable challenges when wireless technologies are applied to the smart gird, e.g., the tradeoffs between wireless coverage and capacity, the high reliability requirement for communication, and limited spectral resources. Relaying has emerged as one of the most promising candidate solutions for addressing these issues. In this article, an introduction to various relaying strategies is presented, together with a discussion of how to improve spectral efficiency and coverage in relay-based information and communications technology (ICT) infrastructure for smart grid applications. Special attention is paid to the use of unidirectional relaying, collaborative beamforming, and bidirectional relaying strategies.
We describe the architecture and algorithms of the Adaptive Charging Network (ACN), which was first deployed on the Caltech campus in early 2016 and is currently operating at over 100 other sites in the United States. The architecture enables real-time monitoring and control and supports electric vehicle (EV) charging at scale. The ACN adopts a flexible Adaptive Scheduling Algorithm based on convex optimization and model predictive control and allows for significant over-subscription of electrical infrastructure. We describe some of the practical challenges in real-world charging systems, including unbalanced three-phase infrastructure, non-ideal battery charging behavior, and quantized control signals. We demonstrate how the Adaptive Scheduling Algorithm handles these challenges, and compare its performance against baseline algorithms from the deadline scheduling literature using real workloads recorded from the Caltech ACN and accurate system models. We find that in these realistic settings, our scheduling algorithm can improve operator profit by 3.4 times over uncontrolled charging and consistently outperforms baseline algorithms when delivering energy in highly congested systems.
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