Online optimal variable charge-rate coordination of plug-in electric vehicles to maximize customer satisfaction and improve grid performance

Abstract

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=5min to maximize the customers' satisfactions for all PEV owners based on their requested plug-out times, requested battery state of charges (SOC Req ) 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.

Optimal fixed charge–rate coordination of plug‐in electric vehicle incorporating capacitor and OLTC switching to minimize power loss and voltage deviation

“…. The fixed charge–rate coordination strategy with capacitor and OLTC switching is faster than . From the Fig.…”

“…In this proposed system, there are 30% of PEVs with a 3.3 kW and 6 kWh charger and battery size, respectively; 40% of PEVs with a 6.6 kW and 16 kWh charger and battery size, respectively; and 30% of PEVs with a 7.2 kW and 19.2 kWh charger and battery size, respectively. To validate the accuracy of the proposed method, a case study based on the use of a variable charge–rate coordination (VCC) strategy is adopted. In addition, three different cases are investigated.…”

“…From the Fig. 7(a), it can be seen that the last vehicle of the weakest feeder started to charge at 2:10, and all the vehicles were fully charged by 3 (Table I, case B) [11], this proposed method with fixed charge-rate improves the voltage profile of the weakest node remarkably, particularly during peak hours, as shown in Fig. 8(a) and Table II.…”

“…A two‐stage optimization procedure is proposed in to charge the PEV optimally considering customer satisfaction as well as charging cost minimization. Moreover, an optimal variable charge–rate scheduling of PEVs is introduced to ensure priority fairness for all PEVs in distribution.…”

Optimized sizing of photovoltaic grid‐connected electric vehicle charging system using particle swarm optimization

“…The growing greenhouse gas emissions and the uncertainties in the oil prices trigger immense interest in electric vehicle (EV) . Many large automobile manufacturers have taken the initiatives to develop their own EV—some of which have entered the commercial market, while many are on the verge of launching their products.…”