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Electric vehicles are increasingly connected to distribution feeders whose transformers, cables, and voltage-control devices were not originally designed for coincident residential charging. Smart charging coordination can convert this challenge into an operational flexibility resource when user needs and network limits are treated jointly. This case study applies stochastic EV charging coordination to a realistic distribution-network testbed to reduce evening peaks, improve voltage profiles, and support local flexibility procurement. The objective is operational rather than purely algorithmic: the study evaluates what a distribution system operator and an aggregator can learn from coordinated charging under uncertain conditions. The case uses empirical-style charging-session statistics, a radial low-voltage feeder model, and stochastic scenarios for arrival time, departure time, energy demand, and feeder loading. Uncontrolled charging, deterministic coordination, and stochastic coordination are compared using the same network and mobility assumptions. The stochastic formulation is evaluated through network-level indicators for feeder peak demand, transformer loading, voltage deviation, flexibility availability, and procurement cost. The comparison is structured to show whether uncertainty-aware coordination is more robust than deterministic scheduling when charging behavior and grid conditions deviate from forecasts. Stochastic EV charging coordination is a practical planning and operational tool for distribution networks with rising residential and workplace charging demand. The case study provides implementation lessons for DSOs, aggregators, and flexibility-market designers.

Electric vehicle charging imposes new operational stress on distribution grids, especially where local transformer capacity and feeder voltage margins are limited. Grid-edge intelligence could enable charging decisions to be made closer to the physical constraints that shape those impacts. Existing EV charging control architectures often depend on centralized optimization, cloud-side visibility, or stable communication paths. Such designs can create privacy, latency, and resilience gaps when local operating conditions change rapidly. This article proposes a layered grid-edge intelligence architecture for distributed EV charging control. The architecture integrates local forecasting, federated model updates, and resilience-aware coordination into a unified system design. The architecture is developed conceptually through functional layers, interfaces, data flows, and operational workflows. Implementation scenarios and validation stages are outlined to guide future simulation, hardware-in-the-loop testing, and field deployment. The proposed architecture defines local forecasting nodes, a federated update layer, and resilience coordination functions that interact through structured protocols. Five tables are specified across the full manuscript to summarize model roles, coordination strategies, resilience mechanisms, deployment scenarios, and validation procedures. The architecture offers a blueprint for grid-edge EV charging management that balances intelligence, privacy, and resilience. It is intended to support future practical implementation rather than report experimental results.

Peer-to-peer solar trading can empower prosumers but poses congestion risks for distribution networks. A bilevel framework that separates market functions from network oversight can enable secure, efficient transactive energy. This article proposes a conceptual bilevel transactive energy framework that coordinates P2P solar trading, EV charging flexibility, and active congestion management. The framework is intended as an architectural contribution rather than a mathematical optimization model. The framework is designed conceptually, with an upper market layer and a lower network operation layer. Information flows, decision logics, and validation pathways are described without mathematical formulation. The framework defines clear roles for market participants and the DSO, integrates EV flexibility as a congestion-mitigation resource, and includes a feedback mechanism from network to market. Five tables summarize the architecture, component functions, and validation plan. The proposed conceptual architecture can guide future software implementations, regulatory sandbox designs, and field pilots. It bridges the gap between market innovation and grid reliability.

The growing adoption of electric vehicles (EVs) and the expansion of their charging infrastructure offer substantial potential to advance sustainable mobility in smart cities. Nevertheless, the highly interconnected design of EV charging stations (EVCSs) renders this infrastructure vulnerable to multiple cybersecurity threats, such as false data injection, man-in-the-middle attacks, malware infections, and denial-of-service attacks. Moreover, financial threats—including fraudulent billing and credit card data theft—pose significant risks to EV users. In this study, we introduce a blockchain network built on Hyperledger Fabric tailored for EVCSs to address these vulnerabilities. The proposed network employs smart contracts to securely and transparently handle essential operations, including authentication, charging session oversight, and payment confirmation. By identifying and preventing unauthorized data alterations or access attempts, the blockchain framework enhances the robustness of EVCS networks. A before-and-after comparative evaluation of the implemented blockchain network illustrates its effectiveness in countering prevalent cyberattacks targeting EVCS infrastructure. The evaluation encompasses performance indicators derived from the Hyperledger Caliper benchmarking tool, revealing the solution’s minimal latency suitable for real-time applications and its capacity to scale in line with expanding EV infrastructure demands. Implementing this blockchain-augmented security solution is anticipated to boost user confidence and dependability in EVCS systems. 

This paper presents a two-stage approach that leverages energy savings from optimal network reconductoring to determine the appropriate sizing and placement of electric vehicle (EV) charging loads in residential areas of urban distribution networks. In the initial stage, the Flower Pollination Algorithm (FPA) was used to reduce annual energy losses in the radial distribution system through optimal reconductoring. A multi-objective function was created to simultaneously minimize the investment cost, peak power loss, and annual energy loss cost across various load factors. Outcomes from the flower pollination algorithm were benchmarked against those from the particle swarm optimization algorithm. In the subsequent stage, a straightforward heuristic was introduced to allocate and size the EV charging demand at each node, using a share of the annual energy savings realized from the optimal reconductoring. The quantity of electric cars, electric bikes, and electric scooters that could be supported at individual nodes was calculated without violating voltage or branch current limits. The proposed method was tested through simulations on the 123-bus and 51-bus radial distribution networks to confirm its validity and performance.

As electric vehicles (EVs) steadily gain popularity on public roads, commercial EV charging has become a routine business service. As this trend continues, charging station operators are actively exploring strategies to boost the profitability of their services or to distribute limited resources more effectively. Dynamic pricing has proven effective for increasing revenue in markets with varying customer demand. This paper introduces a Markov Decision Process (MDP)-based framework for dynamic pricing that maximizes either revenue or utilization for charging station operators. The proposed method is implemented through a Monte Carlo Tree Search (MCTS) algorithm. It is tested in simulation across multiple scenarios derived from a real-world dataset of EV charging sessions. Results demonstrate that the approach delivers near-optimal pricing decisions within milliseconds, even for large-scale instances, delivering substantial improvements in revenue or utilization compared to a conventional flat-rate baseline across various parameter settings.