Abstract
A heuristic load management (H-LMA) algorithm is presented for coordination of Plug-in Electric Vehicles (PEVs) in distribution networks to minimize system losses and regulate bus voltages. The impacts of optimization period T (varied from 15 minutes to 24 hours) and optimization time interval (varied 15 minutes to one hour) on the performance, accuracy and speed of the H-LMA is investigated through detailed simulations considering enormous scenarios. PEV coordination is performed by considering substation transformer loading while taking PEV owner priorities into consideration. Starting with the highest priority consumers, HLMA will use time intervals to distribute PEV charging within three designated high, medium and low priority time zones to minimize total system losses over period T while maintaining network operation criteria such as power generation and bus voltages within their permissible limits. Simulation results generated in MATLAB are presented for a 449 node distribution network populated with PEVs in residential feeders.
Highlights
Preliminary studies by Amin et al (2005), Amin (2008) and Lightner et al (2010) indicate that Plug-In Electric Vehicles (PEVs) will dominate the market in the near future as pollution-free alternatives to the conventional petroleum- based transportation
A heuristic load management (H-LMA) algorithm is presented for coordination of Plug-in Electric Vehicles (PEVs) in distribution networks to minimize system losses and regulate bus voltages
If at any time the load flow indicates a constraint violation at any node (Eqs. 3-4), the algorithm will try the possible charging start time such that the constraints are satisfied. It may not be possible for all PEV owners to be accommodated in their preferred charging zones and must be deferred to the possible hour. Once it has been determined which PEV node in that priority group can begin charging and at what time resulting in minimum system losses, the selected PEV scheduling is permanently assigned and the system load curve updated ready for the iteration
Summary
Preliminary studies by Amin et al (2005), Amin (2008) and Lightner et al (2010) indicate that Plug-In Electric Vehicles (PEVs) will dominate the market in the near future as pollution-free alternatives to the conventional petroleum- based transportation. The algorithm assumes all PEVs are plugged in at 18:00 (6pm) It begins by first reading the input parameters (e.g., bus and branch impedance data, nodes with PEVs, optimization period T, optimization time interval ∆t , designated priority time zones, load profiles for PEV chargers and residential loads as well as system constraints) and performing initialization (e.g., selecting the highest priority group, time zone and PEV). It may not be possible for all PEV owners to be accommodated in their preferred charging zones and must be deferred to the possible hour Once it has been determined which PEV node in that priority group can begin charging and at what time resulting in minimum system losses, the selected PEV scheduling is permanently assigned and the system load curve updated ready for the iteration. A fixed charging power of 4 kW is used
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