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Demand Side Management for Thermally Activated Building Systems Based on Multiple Linear Regression

Journal of Electronic Science and Technology 2015年第4期13卷 355-360页

作者： Martin Schmelas Julien H?ll elmar bollin the Institute of Energy System Techniques Offenburg University of Applied Sciences the Department of Microsystems Engineering University of Freiburg

The building sector and its heating and cooling represents one of the major consumer of energy worldwide. Simultaneously, the share of fluctuating generation of renewable energies in the energy mix increases. Therefore storage and demand side management technologies are required. The new adaptive and predictive control algorithm for thermally activated building systems （TABS） based on multiple linear regression （AMLR） presented in this paper enables the application of demand side management （DSM） strategies. Based on simulations, different strategies have been compared with each other. By applying the AMLR algorithm, electricity energy cost savings of 38% could be achieved compared to the conventional control strategy for TABS, while increasing the thermal comfort. At the same time, thermal energy demand can be reduced in the range between 4% to 8%, and pump operation time from 86% to 89%.

关键词： Demand side management smartgrid thermal storage thermally activated buildingsystems.

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An Overview to the Concept of Smart Coupling and Battery Management for Grid Connected Photovoltaic Battery System

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Journal of Electronic Science and Technology 2015年第4期13卷 367-372页

作者： Deepranjan Dongol elmar bollin Thomas Feldmann the Institute of Energy System(INES) Offenburg University of Applied Science the Institute of Energy System(INES) Offenburg University of Applied Science

The paper gives an overview on the need for smart coupling for battery management in grid integrated renewable energy system （RES）. Grid integrated photovoltaic （PV） battery system, as being popular and extensively used has been discussed in the paper. Smart coupling refers to intelligent grid integration such that it can foresee local network conditions and issue battery power flow management strategy accordingly to shave the peak PV and peak load. Therefore, a need for predictive energy management arises for smart integration to the grid and supervision of the power flow in accordance to the grid conditions. This is also a running project at the Institute of Energy Systems （INES）, Offenburg University of Applied Science, Germany since January, 2015. The paper should provide insights to the motivation, need and gives an outlook to the features of desired predictive energy management system （PEMS）.

关键词： Battery management optimization,predictive energy management smart coupling

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