Load Management for the household sector

Analysis of load management instruments for the energy demand of the household sector – under consideration of technological change

PhD candidate: Martin Bock

Current situation

The balance between supply and demand in the electric grit is becoming a considerable challenge. On the one hand, the use of renewable energy causes a volatile and hardly manageable energy supply. On the other hand, the demand side is characterized by a politically motivated diffusion of energy-intensive technologies in the household sector, such as battery-electric vehicles (BEV) and heat pumps. Due to a high potential of simultaneity, further peak loads occur. The energy demand is influenced by energy suppliers via load management. This can be implemented either incentive based or control based. The incentive based concept aims to motivate the consumers to change their behavior, whereas the control based solution includes the direct control of individual devices, for example of night storage heating systems.

Project goal

Against this background, the aim of this research project is the investigation of the effects of load management instruments on the energy demand of households with a particular regard to new technologies. Therefore, the reproduction of the energy demand of households has to include behavior-based and technological issues. To reproduce substitution processes of households, it is necessary to model the different types of energy, their convertibleness and storage properties explicitly. Finally, suitable instruments for an application by energy suppliers have to be identified.

Approach

To achieve this goal, an agent based model is developed to reproduce the interaction of energy suppliers and households concerning the load management. The approach can be subdivided into three parts. First, a population of heterogeneous households and energy suppliers is produced. The second step includes the simulation of the behavior of households without the usage of load management and the calculation of the unaffected household energy consumption. As part of the third step, each household is described as a mixed-integer problem. To outline the results on load management, each household optimizes its behavior concerning energy costs and loss of comfort in a combined energy flow and scheduling model.