This chapter details the principles applied to represent optimal charging of PHEVs by employing an agent-based model that simulates the travelling patterns of vehicles on a road network. The output data from the temporal and spatial movement of vehicles is used as a reliable forecast so the time-coordinated optimal power flow (TCOPF) program can devise optimal charging scenarios of PHEVs in a local electrical network. The effectiveness of the model is illustrated by presenting a multiday case study in an urban area. Results show a high level of detail and variability in PHEV charging when a present-day carbon fuel mix is compared to one with lower carbon intensity.

Chapter Contents:

• 7.1 Modelling PHEV mobility
• 7.1.1 Modelling methods
• 7.2 Combining agent-based and load flow models
• 7.2.1 Agent-based model for vehicles
• 7.2.2 PHEV optimal power flow formulation
• 7.2.2.1 For PHEV charging cost minimisation scenario
• 7.3 ABM-TCOPF case study for charging of PHEVs
• 7.3.1 Input data and assumptions
• 7.3.1.1 Driver profiles
• 7.3.1.2 PHEV features
• 7.3.1.3 City layout
• 7.3.1.4 Electricity load profiles and network characteristics
• 7.3.2 Case studies and energy system parameters
• 7.4 Techno-economical results
• 7.4.1 Agent-based model results
• 7.4.2 Optimal power flow model results

Inspec keywords: secondary cells; road vehicles; battery powered vehicles

Other keywords: local electrical network; time-coordinated optimal power flow program; PHEV optimal charging; road network; vehicle temporal movement; energy service networks; electric vehicle mobility; vehicle spatial movement; present-day carbon fuel mix; TCOPF program

Subjects: Transportation; Secondary cells