Alleviating low-voltage network congestion using electric vehicles and heat pumps: an agent-based modeling approach

Abstract

This thesis investigates the integration of multiple explicit and implicit flexibility incentives at the household level to identify a cost-effective solution to mitigate congestion on low-voltage (LV) networks using electric vehicles and heat pumps. Explicit flexibility allows for real-time adjustments in energy usage during periods of congestion, while implicit flexibility involves shifting demand based on price signals such as Time-of-Use tariffs and the Day-ahead market. An agent-based modeling approach is used to simulate household energy consumption patterns and responses to these flexibility incentives.

The findings showed that implicit flexibility is effective up to a 20% participation rate in reducing congestion and resulted in the highest cost savings. However, beyond this participation point effectiveness diminished eventually creating more congestion. Explicit flexibility provided more consistent congestion relief. The combination of both flexibility types enhances congestion mitigation and also increases cost savings for households compared to explicit flexibility, while comfort can be maintained using comfort limits. This integrated approach promotes a more efficient LV network system, benefiting both network operators and residents.