Agent-based modelling of electric vehicle charging for optimized charging station operation
Widespread adoption of electric vehicles (EVs) would significantly increase the overall electrical load demand in power distribution networks. Hence, there is a need for comprehensive planning of charging infrastructure in order to prevent power failures or scenarios where there is a considerable...
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| Format: | Thesis |
| Language: | English |
| Published: |
2019
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| Online Access: | https://hdl.handle.net/10356/101802 http://hdl.handle.net/10220/48574 |
| Summary: | Widespread adoption of electric vehicles (EVs) would significantly increase the overall
electrical load demand in power distribution networks. Hence, there is a need for
comprehensive planning of charging infrastructure in order to prevent power failures
or scenarios where there is a considerable demand-supply mismatch. Accurately predicting
the realistic charging demand of EVs is an essential part of the infrastructure
planning. Charging demand of EVs is influenced by several factors such as driver behavior,
location of charging stations, electricity pricing etc. In order to implement an
optimal charging infrastructure, it is important to consider all the relevant factors which
influence the charging demand of EVs. Several studies have modelled and simulated the
charging demands of individual and groups of EVs. However, in many cases, the models
do not consider factors related to the social characteristics of EV drivers. Other studies
do not emphasize on economic elements. This thesis aims at evaluating the effects of
the above factors on EV charging demand using a simulation model. An agent-based
approach using the NetLogo software tool is employed in this thesis to closely mimic
the human aggregate behaviour and its influence on the load demand due to charging of
EVs.
EV charging stations where the EV charging takes place will play an important
role in the energy management of smart cities. Private and commercial EV charging
loads would further stress the distribution system. Photovoltaic (PV) systems, which
can reduce this stress, also show variation due to weather conditions. Hence, after the
successful modelling of EV charging behavior using agent based approaches, a hybrid
optimization algorithm for energy storage management is proposed as an application.
This algorithm shifts its mode of operation between the deterministic and rule-based approaches
depending on the electricity price band allocation. The cost degradation model
of the energy storage system (ESS) along with the levelized cost of PV power is used in
the case of PV integrated charging stations with on-site ESS. The algorithm comprises
three parts: categorization of real-time electricity price in different price bands, real-time
calculation of PV power from solar irradiation data and optimization for minimizing the
operating cost of an EV charging station integrated with PV and ESS. An extensive
simulation study is carried out with private and commercial EV charging load model
obtained from the agent based modeling approach, in the context of Singapore, to check
the effectiveness of this algorithm. Furthermore, a detailed analysis of the subsidy and
incentive to be given by the government agencies for a higher penetration of PV systems
is also presented. This work would aid in planning of adoption of PV integrated EV
charging stations with on-site ESS which would be expected to take place of traditional
gas stations in future. |
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