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152 B. Becker et al. Furthermore, the extent of renewable energy sources, like solar power and wind turbines, becomes more and more significant. The former centralized power plant structure will partially be transformed into a distributed generation system. This development is not fully compatible with the current grid structure. A typical suburban low voltage grid structure consists of a transformer, several loads, and generators. Local demand is partly satisfied by renewable energy sources. Unfortunately, demand does not comply with the power generation. Renewable power is quite often provided in a period of low demand. This results in increased grid losses, because power has to be transformed to the upper voltage level. To avoid the afore mentioned bottlenecks and to efficiently integrate renewable energy sources into the grid, an intelligent load management is mandatory. Furthermore, it is necessary to evolve from a demand depending generation to a generation depending demand. To reduce the peak demand, shown in Fig. 1, control of demand and generation is necessary. In [3], an external management structure of the low voltage grid in a residential area is addressed, whereas this paper has its focus on managing the in-house appliances with respect to energy efficiency. Increasing the resident’s comfort is not the central point of this approach. Therefore, we introduce a hierarchical structure; alternatively a peer-to-peer approach is presented in [5]. In this approach, several pools of appliances are formed to balance demand sets and power generation. Such a pool contains a set of decentralized power suppliers and consumers. The needed balance can be achieved, if the consumers’ demand set profiles are adjusted to the power generation in the pool. 3 Demonstrating Environment To analyse the suggested improvements on real hardware components, a demonstrating environment has been developed. It represents some significant electrical components of modern smart-home architectures, as is evident from Fig. 2. The entire assembly is connected via electric powerline and supports a maximum of nine electrical consumers, of which six are constructed to behave like intelligent appliances. The in-house-installation is controlled by the smart-home management device (SHMD). The intelligent appliances are connected through six relays so that each of them can be switched on or off independently. To be called intelligent, they should be aware of their current situation and reasonably respond to it. Each intelligent consumer has its own sensory equipment to get electrical data like voltage, current, and active power. The intelligent responses of each appliance are realized by an embedded system, which periodically polls sensors and provides averages of the measured values via HTTP-interface in XML format separately for each intelligent appliance. The sum of all consumers’ power consumption in the smart-home can also be requested at the smart power meter, continuously. Additionally, the local controller unit of each appliance receives control-signals from the SHMD to toggle
Decentralized Energy-Management to Control Smart-Home Architectures 153 smart-home management device (SHMD) controller R1 Ø controller R4 Ø data storage intelligent appliances controller R2 Ø controller R5 Ø communication electrical power controller R3 Ø controller R6 Ø low-voltage-grid Fig. 2. Demonstrating environment conventional appliances smart power meter chargecontroller the on-state of its connected intelligent appliance. In this way, a hierarchically structured observer/controller architecture, cf. Sect. 4, is used. In the demonstrating environment, it is assumed that an electric vehicle is present in the smart-home. For that purpose, a lead-acid storage battery with a capacity of 4 kWh is connected to the smart-home demonstrator. It represents the mobile energy storage of the electric vehicle. The charging- and discharging process is controlled and observed by a special charge controller which is connected via powerline to the SHMD. The SHMD is the central device in the scenario presented here. Any relevant data, like current power-requirement of each intelligent appliance, the system’s voltage, or the current total power of the smart-home, are collected in its database. Based on the content of the database, a user interface is generated by the SHMD’s web-server to inform the smart-home’s resident about its current condition and optimization potential. It allows displaying and analyzing the powerdata of elapsed time-slots. Furthermore, the web-interface permits the resident to define the degrees of freedom for each intelligent appliance. This value describes the potential of controlling the device’s on-state automatically. The main challenge of future energy management is balancing supply and demand in energy grids. Therefore, smart algorithms are needed to schedule the electrical consumers and especially the car’s charging process. A large number of decentralized batteries permits to feed electrical energy back into the grid.
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Lecture Notes in Computer Science 5
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Volume Editors Christian Müller-Sc
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General Chair Organization Christia
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Organization IX Hartmut Schmeck Kar
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Keynote Table of Contents HyVM - Hy
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Table of Contents XIII JetBench:AnO
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How to Enhance a Superscalar Proces
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Abdullah, Tariq 174 Alima, Luc Onan
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