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A conventional electric vehicle charger (EVC) charges only one EV concurrently. This leads to underutilization whenever the charging power is less than the EVC-rated capacity. Consequently, the cost-effectiveness of conventional EVCs is limited. Reconfigurable EVCs (REVCs) are a new technology that overcomes underutilization by allowing multiple EVs to be charged concurrently. This brings a cost-effective charging solution, especially in large car parks requiring numerous chargers. Therefore, this paper proposes an optimal planning strategy for car parks deploying REVCs. The proposed planning strategy involves three stages. An optimization model is developed for each stage of the proposed planning strategy. The first stage determines the optimal power rating of power modules inside each REVC, and the second stage determines the optimal number and configuration of REVCs, followed by determining the optimal operation plan for EV car parks in the third stage. To demonstrate the effectiveness of the proposed optimal planning strategy, a comprehensive case study is undertaken using realistic car parking scenarios with 400 parking spaces, electricity tariffs, and grid infrastructure costs. Compared to deploying other conventional EVCs, the results convincingly indicate that the proposed optimal planning strategy significantly reduces the total cost of investment and operation while satisfying charging demands. [ABSTRACT FROM AUTHOR]

Distribution networks with high electric vehicle (EV) penetration levels can experience transformer overloading and voltage instability issues. A charge scheduling approach is proposed to mitigate against these issues that suits smart home settings in residential areas. It comprises measurement systems located at distribution transformers that communicate directly with fuzzy logic controller (FLC) systems embedded within EV supply equipment (EVSE). This realizes a reduction in data processing requirements compared to more centralized control approaches, which is advantageous for distribution networks with large numbers of transformers and EV scheduling requests. A case study employing the proposed approach is presented. Realistic driver behavior patterns, EV types, and multivariate probabilistic modeling were used to estimate EV charging demands, daily travel mileage, and plug-in times. A Monte Carlo simulation approach was developed to obtain EV charging loads. The effectiveness of mitigation in terms of reducing distribution transformer peak load levels and losses, as well as improving voltage stability is demonstrated for a distribution network in Jakarta, Indonesia. [ABSTRACT FROM AUTHOR]