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.
Online optimal variable charge-rate coordination of plug-in electric vehicles to maximize customer satisfaction and improve grid performance
- Authors: Hajforoosh, Somayeh , Masoum, Mohammad , Islam, Syed
- Date: 2016
- Type: Text , Journal article
- Relation: Electric Power Systems Research Vol. 141, no. (2016), p. 407-420
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- Description: Participation of plug-in electric vehicles (PEVs) is expected to grow in emerging smart grids. A strategy to overcome potential grid overloading caused by large penetrations of PEVs is to optimize their battery charge-rates to fully explore grid capacity and maximize the customer satisfaction for all PEV owners. This paper proposes an online dynamically optimized algorithm for optimal variable charge-rate scheduling of PEVs based on coordinated aggregated particle swarm optimization (CAPSO). The online algorithm is updated at regular intervals of Δt = 5 min to maximize the customers’ satisfactions for all PEV owners based on their requested plug-out times, requested battery state of charges (SOCReq) and willingness to pay the higher charging energy prices. The algorithm also ensures that the distribution transformer is not overloaded while grid losses and node voltage deviations are minimized. Simulation results for uncoordinated PEV charging as well as CAPSO with fixed charge-rate coordination (FCC) and variable charge-rate coordination (VCC) strategies are compared for a 449-node network with different levels of PEV penetrations. The key contributions are optimal VCC of PEVs considering battery modeling, chargers’ efficiencies and customer satisfaction based on requested plug-out times, driving pattern, desired final SOCs and their interest to pay for energy at a higher rate.
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
- Reviewed:
- 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.