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.
Optimal scheduling of LTC and switched shunt capacitors in smart grid concerningovernight charging of Plug-in Electric Vehicles
- Authors: Deilami, Sara , Masoum, Amir , Masoum, Mohammad , Abu-Siada, Ahmed , Islam, Syed
- Date: 2015
- Type: Text , Conference proceedings , Conference paper
- Relation: AASRI International Conference on Applied Engineering Science, ICAES 2014; Los Angeles, United States; 23rd-24th July 2014 p. 71-76
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- Description: It is well-known that load variation and nonlinearity have detrimental impacts on the operation and performance of the conventional power systems and future smart grids (SGs) including their voltage profiles, power quality, losses and efficiency particularly during the peak load hours. This paper will perform optimal scheduling of transformer load tap changer (LTC) and switched shunt capacitors (SSCs) in smart grid with nonlinear loads and plug-in electric vehicle (PEV) charging activities to improve voltage profile, reduce grid losses and control the total harmonic distortion (THD). An established genetic algorithm (GA) for the dispatch of LTC/SSC and a recently implemented algorithm based on maximum sensitivity selections (MSS) optimization for coordination of PEVs are used to perform detailed simulations and analyses.
Overnight coordinated charging of plug-in electric vehicles based on maximum sensitivities selections
- Authors: Masoum, Amir , Deilami, Sara , Masoum, Mohammad , Abu-Siada, Ahmed , Islam, Syed
- Date: 2015
- Type: Text , Conference proceedings , Conference paper
- Relation: AASRI International Conference on Applied Engineering Science, ICAES 2014; Los Angeles, United States; 23rd-24th July 2014 p. 65-70
- Full Text: false
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- Description: The future smart grid (SG) will be populated with high penetrations of plug-in electric vehicles (PEVs) that may deteriorate the quality of electric power. The consumers will also be seeking economical options to charge their vehicles. This paper proposes an overnight maximum sensitivities selection based coordination algorithm (ON-MSSCA) for inexpensive overnight PEV charging in SG. The approach is based on a recently implemented online algorithm (OL-MSSCA) that charges the vehicles as soon as they are randomly plugged-in while considering SG generation, demand and voltage constraints. In contrast to the online approach, ON-MSSCA relies on inexpensive off-peak load hours charging to reduce the cost of generating energy such that SG constraints are not violated and all vehicles are fully charged overnight. Performances of the online and overnight algorithms are compared for the modified IEEE 23kV distribution system with low voltage residential feeders populated with PEVs.
Online coordination of plug-in electric vehicle charging in smart grid with distributed wind power generation systems
- Authors: Masoum, Amir , Deilami, Sara , Masoum, Mohammad , Abu-Siada, Ahmed , Islam, Syed
- Date: 2014
- Type: Text , Conference proceedings , Conference paper
- Relation: 2014 IEEE Power and Energy Society General Meeting; National Harbor, United States; 27th-31st July 2014 Vol. 2014, p. 1-5
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- Description: Plug-in electric vehicles (PEVs) and wind distributed generations (WDGs) will represent key technologies in the future smart grid configurations. PEV charging at high penetration levels requires substantial grid energy that can be partially supplied by WDGs. This paper examines the impacts of WDGs on performance of recently implemented online maximum sensitivities selection based coordination algorithm (OL-MSSCA) for PEV charging. The algorithm considers random arrivals of vehicles and time-varying market energy price to reduce the total cost of energy generation for PEV charging and the associated grid losses while providing consumer priorities based on defined charging time zones. OL-MSSCA will be improved to also consider DGs while maintaining network operation criteria such as maximum generation limits and voltage profiles within their permissible limits. Detailed simulation is performed on the modified IEEE 23kV distribution system with three WDGs and 22 low voltage residential networks populated with PEVs. The main contributions of this paper are inclusion of WDGs in OL-MSSCA, as well as detailed investigations on the impacts of their peak generation times, penetrations and locations on the performance of smart grid populated with PEVs.
- Description: IEEE Power and Energy Society General Meeting