Modelling interactions between distributed energy technologies and the centralised electricity supply system
Renewable electricity generators, such as solar photovoltaics (PVs), and variation management technologies, such as battery storage and demand response (DR) systems, can be deployed in a distributed fashion, which can benefit the overall system. Such distributed energy technologies interact with and influence the centralised generation and transmission systems. This thesis investigates these interactions using a cost-minimising investment model (ELIN) to generate scenarios for the future European electricity supply system and analysing the operation of the system in an economic dispatch model (EPOD).
Using the EPOD model to study congestion in the European transmission system, we show that while demand-related congestion can be reduced with DR, congestion related to wind power production cannot. Results also demonstrate that solar and wind power correlate with congestion on different time scales. Solar power cross-correlates with hourly congestion with a time displacement of 6-9 hours, whereas wind power correlates with congestion on a weekly time scale.
Two approaches are applied to model the effect of household-level phenomena on the centralised electricity supply system. First, a model for electric space heating load is integrated into EPOD, in order to study DR. The results show that DR in Swedish single-family dwellings (SFDs) primarily reduces the system running costs in neighbouring regions outside Sweden. Second, to capture market feedback, a cost-minimising investment model for PVs and batteries for individual households is iteratively linked to EPOD, yielding optimal capacities of up to around 8 GWp of PVs and 8 GWh of batteries in total for Swedish SFDs. It is concluded that capturing market feedback is crucial for avoiding overestimations of the household investments.
energy systems modelling