A. Masoum

Real-Time Coordination of Plug-In Electric Vehicle Charging in Smart Grids to Minimize Power Losses and Improve Voltage Profile

This paper proposes a novel load management solution for coordinating the charging of multiple plug-in electric vehicles (PEVs) in a smart grid system. Utilities are becoming concerned about the potential stresses, performance degradations and overloads that may occur in distribution systems with multiple domestic PEV charging activities. Uncontrolled and random PEV charging can cause increased power losses, overloads and voltage fluctuations, which are all detrimental to the reliability and security of newly developing smart grids. Therefore, a real-time smart load management (RT-SLM) control strategy is proposed and developed for the coordination of PEV charging based on real-time (e.g., every 5 min) minimization of total cost of generating the energy plus the associated grid energy losses. The approach reduces generation cost by incorporating time-varying market energy prices and PEV owner preferred charging time zones based on priority selection. The RT-SLM algorithm appropriately considers random plug-in of PEVs and utilizes the maximum sensitivities selection (MSS) optimization. This approach enables PEVs to begin charging as soon as possible considering priority-charging time zones while complying with network operation criteria (such as losses, generation limits, and voltage profile). Simulation results are presented to demonstrate the performance of SLM for the modified IEEE 23 kV distribution system connected to several low voltage residential networks populated with PEVs.

Fuzzy Approach for Online Coordination of Plug-In Electric Vehicle Charging in Smart Grid

This paper proposes an online fuzzy coordination algorithm (OL-FCA) for charging plug-in electric vehicles (PEVs) in smart grid networks that will reduce the total cost of energy generation and the associated grid losses while maintaining network operation criteria such as maximum demand and node voltage profiles within their permissible limits. A recently implemented PEV coordination algorithm based on maximum sensitivity selection (MSS) optimization is improved using fuzzy reasoning. The proposed OL-FCA considers random plug-in of vehicles, time-varying market energy prices, and PEV owner preferred charging time zones based on priority selection. Impacts of uncoordinated, MSS, and fuzzy coordinated charging on total cost, gird losses, and voltage profiles are investigated by simulating different PEV penetration levels on a 449-node network with three wind distributed generation (WDG) systems. The main advantage of OL-FCA compared with the MSS PEV coordination is the reduction in the total cost it introduces within the 24h.

Performance of heuristic optimization in coordination of plug-in electric vehicles charging

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.

Finite Element Performance Evaluation of Online Transformer Internal Fault Detection Based on Instantaneous Voltage and Current Measurements

AbstractThis paper investigates the performance of a recently proposed online transformer internal fault detection technique through detailed non-linear three-dimensional finite element modelling of the windings, magnetic core and transformer tank. The online technique considers correlation of transformer instantaneous input and output voltage difference and input current at the power frequency and uses the ellipse shape ΔV-I locus as the finger print of the transformer that could be measured every cycle to identify any incipient faults. The technique is simple, fast and suitable for online monitoring of in-service transformers. A detailed three-dimensional finite element model of single-phase transformer is developed and various physical winding deformations with different fault levels are simulated to assess their impacts on the online ΔV-I locus. As transformer field testing under various internal fault conditions cannot be easily conducted, the main contributions of this paper are accurate finite elem...

Online coordination of plug-in electric vehicle charging in smart grid with distributed wind power generation systems

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.

Optimal scheduling of load tap changer and switched shunt capacitors in smart grid with electric vehicles and charging stations

Random charging of plug-in electric vehicles (PEVs) particularly during the peak load hours could impairment the performance of future smart grids. This paper presents genetic algorithms (GAs) for optimal scheduling of LTC and switched shunt capacitors (SSCs) to improve the performance of smart grid with PEV charging at consumer premises in residential feeders and PEV charging stations (PEV-CSs) in distribution networks. The forecasted daily load curves associated with PEV-CSs and residential feeders populated with PEVs are first generated and then incorporated in the GA-based optimal LTC and SSC scheduling solution. Simulation results without and with optimal scheduling are presented for a 449 node smart grid system with 5 PEV-CSs considering random and coordinated charging of 264 PEVs in 22 low voltage residential networks.