With the rapid growth of electrified transportation, the design of charging infrastructure and station-level energy management has become increasingly important for meeting growing power and energy demands efficiently and cost-effectively. To address this challenge, this study presents an optimal sizing framework for photovoltaic (PV) and battery energy storage system (BESS) integrated EV charging stations, using an actual battery electric bus (BEB) charging station as the case study. This work formulates the load support fraction as a planning parameter, where different load support fractions (10 to 100)% are evaluated using an annualized-cost-based NPV metric, defined as the present value of annualized net savings to quantify the economic benefits and achieve optimal PV-BESS sizing design that is most profitable over the lifetime, considering seasonal variability. A hybrid bi-level optimization approach is proposed, where the outer Genetic Algorithm (GA) searches for the best PV-BESS size combinations and the inner Linear Programming (LP) model achieves optimal hourly dispatch for each GA candidate, enabling effective energy management. The case study results from a real-world battery electric bus (BEB) charging station operated by Utah Transit Authority (UTA) in Ogden, UT, USA, demonstrate that a 40% load support fraction is optimal and robust to seasonal variations, providing the best balance between the capital costs and long-term savings, and yielding 21.2% lower annual cost compared to a charging station design without PV-BESS and 42.5% higher NPV compared to a fully PV-BESS powered design.
Arifa Sultana, Jackson Morgan, Abdullah Al Mehadi et al.· IEEE Access· 0 citations
The open radio access network (O-RAN) is evolving toward agentic operation, where large language model (LLM)-driven xApps/rApps generate control proposals under operator intents. However, such proposals may be conflicting, infeasible, or hallucinated, and no existing system jointly provides proposal-independent safety, priority-aware reconciliation, and traceable feedback. To this end, we propose a provably safe arbiter, namely xTRUCE, in the near-real-time (Near-RT) RAN intelligent controller for mitigating multi-xApp conflicts in gNB control. We first develop a structured xApp proposal interface and a three-layer constraint hierarchy that places physical limits and operator-defined rules above relaxable performance targets, alongside a dual-timescale control action space. A two-stage arbitration mechanism then minimizes target shortfalls in the operator-priority order to finalize safe E2 actions within the Near-RT latency budget, while returning conflict certificates to xApps and the operator for renegotiation. Finally, we implement xTRUCE in a multi-cell O-RAN use case, and evaluate its multi-process prototype through simulations with live API-backed LLM xApps and over-the-air experiments on OpenAirInterface/FlexRIC-based O-RAN stacks. Results show that xTRUCE ensures gNB control safety with $100\%$ protected services despite severe proposal hallucinations, achieves priority-consistent performance satisfaction under overload, efficiently guides LLM intent renegotiation via certificates, and keeps a delay-safe E2 control loop.
Le Xia, Rose Qingyang Hu, Paul S. Kudyba et al.· 0 citations
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