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Assessing the capability of Swiss Office buildings to provide secondary frequency reserves



Annika Eichler, Georgios Darivianakis


In power systems, having a high level of renewable energy penetration requires a large amount of ancillary services to balance the supply and demand in real time and stabilize the grid. Traditionally, ancillary services are provided by conventional generators. However, with increasing penetration the limits of conventional generators are reached. Thus, current research focuses on alternatives to satisfy the need for ancillary service with the focus on frequency reserves. One possibility under investigation are buildings, whose thermal inertia offers the possibility to provide frequency reserves without affecting the indoor temperature. However, this option is not finally understood, since other work claims that, although the provision of frequency reserves is possibly, it leads to significant energy losses and increased overall consumption.

Project Description

The task of this work is to assess whether it is reasonable to use Swiss commercial buildings to provide secondary reserves to the grid. An extensive simulation study needs to be performed to increase the understanding towards correlating frequency reserves with the building overall consumption. This question depends on the round-trip of the building, defined as the ratio of the energy, which can be retrieved from the building, to what has been stored in the building compared to the baseline consumption. This round-trip efficiency depends on multiple factors as the building type and the installed devices for heating and cooling, the ambient temperature, the characteristics of the reserve signal, the baseline consumption level and the energy-management controller of the building. These factors and their influence on the round-trip efficiency and the baseline consumption are to be analyzed in a simulation study to gain a better understanding.

Weitere Informationen

John Lygeros

Art der Arbeit: Theory (30%), Implementation (70%)
Voraussetzungen: Model predictive control
Anzahl StudentInnen:
Status: taken
Projektstart: Spring Semester 2017
Semester: Autumn Semester 2016