The integration of Distributed Energy Resources (DERs) such as photovoltaic (PV) systems, battery energy storage systems (BESSs), and electric vehicles (EVs) introduces new challenges to distribution networks despite offering opportunities for decarbonization and grid flexibility. This paper proposes a scalable simulation-based framework that combines deterministic nodal hosting capacity analysis with probabilistic Monte Carlo simulations to evaluate and optimize DER integration in diverse feeder types. The methodology is demonstrated using the IEEE 13-bus and 123-bus test systems under full-year time-series simulations. Deterministic hosting capacity analysis shows that individual nodes can accommodate up to 76% of base load from PV sources, while Monte Carlo analysis reveals a network-wide average hosting capacity of 62%. Uncoordinated DER deployment leads to increased system losses, overvoltages, and thermal overloads. In contrast, coordinated integration achieves up to 38.7% reduction in power losses, 25% peak demand shaving, and voltage improvements from 0.928 p.u. to 0.971 p.u. Additionally, congestion-centric optimization reduces thermal overload indices by up to 65%. This framework aids utilities and policymakers in making informed decisions on DER planning by capturing both spatial and stochastic constraints. Its modular design allows for flexible adaptation across feeder scales and configurations. The results highlight the need for node-specific deployment strategies and probabilistic validation to ensure reliable, efficient DER integration. Future work will incorporate optimization-driven control and hardware-in-the-loop testing to support real-time implementation.

With increased usage, individual batteries within the battery pack will begin to show disparate voltage and State of Charge (SOC) profiles, which will impact the time at which batteries become balanced. Commercial battery management systems (BMSs), used in electric vehicles (EVs) and microgrids, typically send out signals suggesting removal of individual batteries or entire packs to prevent thermal runaway scenarios. To reuse these batteries, this paper presents an analysis of an off-the-shelf Orion BMS with a constrained cycling approach to assess the voltage and SOC balancing and thermal performances of such near-to-second life batteries. A scaled-down pack of series-connected batteries in 6s1p and 6s2p topologies are cycled through a combination of US06 drive and constant charge (CC) profiles using an OPAL-RT real-time Hardware-in-the-loop (HIL) simulator. These results are compared with those obtained from the Matlab/Simulink model to present the error incurred in the simulation environment. Results suggest that the close-to-second life batteries can be reused if operated in a constrained manner and that a scaled-up battery pack topology reduces incurred error.

Dynamic wireless charging (DWC) has emerged as a viable approach to mitigate range anxiety by ensuring continuous and uninterrupted charging for electric vehicles in motion. DWC systems rely on the length of the transmitter, which can be categorized into long-track transmitters and segmented coil arrays. The segmented coil array, favored for its heightened efficiency and reduced electromagnetic interference, stands out as the preferred option. However, in such DWC systems, the need arises to detect the vehicle’s position, specifically to activate the transmitter coils aligned with the receiver pad and de-energize uncoupled transmitter coils. This paper introduces various machine learning algorithms for precise vehicle position determination, accommodating diverse ground clearances of electric vehicles and various speeds. Through testing eight different machine learning algorithms and comparing the results, the random forest algorithm emerged as superior, displaying the lowest error in predicting the actual position.