Integrated modelling of agent-based electric vehicles into optimal power flow studies
Abstract
So far research on modelling the travelling patterns of electric vehicles (EVs) lacks technical depth since the variability of individual travel behaviour lack spatial and temporal disaggregated details - this data is needed by operators (DNOs) to assess EV impact at a low voltage level. Thus, it is essential to integrate issues concerning the mobility of EVs with the operational issues facing DNOs. This paper discusses how a bottom-up agent-based modelling (ABM) approach, addressing the mobility of EVs, can be combined with power flow studies at different levels of abstraction. From the DNOs perspective the fact that EVs can move around means loads disappear and may reappear at a different location, which has consequences on the power flows. Hence the data collected from the EV driving patterns are quantified to monitor the state of charge of the batteries. Subsequently, the output collected from the ABM simulations are applied to power system studies by incorporating its data into a time coordinated optimal power flow (TCOPF) program. An example proof-of-concept case study is showcased to demonstrate the relevance of the ABM paradigm and the effectiveness of the TCOPF solver when they are merged for a small network; in this fashion proving interoperability between the models. Preliminary results illustrate the valuable operational information utilities can obtain regarding optimal EV charging strategies when considering an ABM approach to represent EVs in power flow studies.
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