Real-time charging coordination of plug-in electric vehicles based on hybrid fuzzy discrete particle swarm optimization
- Authors: Hajforoosh, Somayeh , Masoum, Mohammad , Islam, Syed
- Date: 2015
- Type: Text , Journal article
- Relation: Electric Power Systems Research Vol. 128, no. (2015), p. 19-29
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- Description: The main impact of uncoordinated plug-in electric vehicle (PEV) charging is adding new time-variant loads that can increase the strains on the generation units, transmission and distribution systems that may result in unacceptable voltage drops and poor power quality. This paper proposes two dynamic online approaches for coordination of PEV charging based on fuzzy genetic algorithm (FGA) and fuzzy discrete particle swarm optimization (FDPSO). The algorithms will minimize the costs associated with energy generation and grid losses while also maximizing the delivered power to PEVs considering distribution transformer loading, voltage regulation limits, initial and final battery state of charges (SOCs) based on consumers' preferences. The second algorithm relies on the quality and speed of DPSO solution for more accurate and faster online coordination of PEVs while also exploiting fuzzy reasoning for shifting charging demands to off-peak hours for a further reduction in overall cost and transformer loading. Simulation results for uncoordinated, DPSO, FGA and FDPSO coordinated charging are presented and compared for a 449-node network populated with PEVs. Results are also compared with the previously published PEV coordinated charging based on maximum sensitivity selections (MSS). Main contributions are formulating the PEVs charging coordination problem and applying different optimization methods including online FGA and FDPSO considering different driving patterns, battery sizes and charging rates, as well as initial SOCs and requested final SOCs. © 2015 Elsevier B.V.
Forecasting plug-in electric vehicles load profile using artificial neural networks
- Authors: Panahi, Delshad , Deilami, Sara , Masoum, Mohammad , Islam, Syed
- Date: 2015
- Type: Text , Conference proceedings , Conference paper
- Relation: 25th Australasian Universities Power Engineering Conference, AUPEC 2015; Wollongong, Australia; 27th-30th September 2015 p. 1-6
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- Description: Plug-in electric vehicles (PEVs) are becoming very popular these days and consequently, their load management will be a challenging issue for the network operators in the future. This paper proposes an artificial intelligence approach based on neural networks to forecast daily load profile of individual and fleets of randomly plugged-in PEVs, as well as the upstream distribution transformer loading. An artificial neural network (ANN) model will be developed to forecast daily arrival time (Ta) and daily travel distance (Dtr) of individual PEV using historical data collected for each vehicle in the past two years. The predicted parameters are then will be used to forecast transformer loading with PEV charging activities. The results of this paper will be very beneficial to coordination and charge/discharge management of PEVs as well as demand load management, network planning and operation proposes. Detailed simulations are presented to investigate the feasibility and accuracy of the proposed forecasting strategy.