Author

Arash Baharvandi

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Open access 2026

Integrated Generation–Transmission Expansion Planning With Uncertain Dynamic Line Ratings, Renewable Energy, and Electric Vehicles

This paper examines the integrated generation and transmission expansion planning (IGTEP) problem in the presence of increasing power system demand, high penetration of renewable energy sources (RES), and growing electric vehicle (EV) charging loads. The proposed framework jointly optimizes investment and operational costs for new generation units and transmission lines, while considering the environmental benefits of RES integration and the impact of EV charging on grid operation. The inherent uncertainties in load demand, EV charging, and renewable generation are addressed through a hybrid stochastic-robust optimization approach. Dynamic thermal line rating (DTLR) is incorporated through a physics-based heat balance model to capture time-varying transmission limits driven by environmental conditions. The framework also tackles the uncertainty in DTLR, incorporating a heuristic linearization technique to reduce model complexity. Unlike conventional IGTEP approaches that rely on static line ratings or treat uncertainties independently, the proposed framework provides a unified and scalable formulation that jointly captures multi-source uncertainties, including renewable variability, EV demand, and weather-driven line ratings. The effectiveness of the proposed approach is demonstrated on the IEEE 6-bus and IEEE 118-bus systems.

Arash Baharvandi, D. Nguyen · IEEE Open Access Journal of... · 0 citations

Preprint Jul 2026

Scenario-Free Uncertainty-Aware DLMP-Based Bilevel Coordination of EV Charging and Reactive Power Support in Distribution Networks

This paper develops a scenario-free uncertainty-aware bilevel optimization framework for coordinated electric vehicle (EV) charging and reactive power support in distribution networks using distribution locational marginal prices (DLMPs). The upper-level EV aggregator jointly schedules active and reactive charging power to minimize charging costs, while the lower-level energy management system performs network-constrained economic dispatch and determines DLMPs subject to feeder and voltage constraints. To capture uncertainties in load demand and photovoltaic (PV) generation, a compact robust counterpart (RC) reformulation is developed that avoids the computational burden of large-scale stochastic programming and conventional robust optimization. Unlike existing robust counterpart methods that primarily assume Gaussian uncertainties, the proposed approach derives a deterministic reformulation for net-demand uncertainty modeled by a normal-minus-beta distribution, providing a more realistic representation of asymmetric load and renewable variability. An exactness lemma preserves the economic interpretation of DLMPs after KKT reformulation and Big-M linearization. EV chargers also provide reactive power support through non-unity power factor operation to improve voltage regulation. Simulation results on the IEEE 33-bus distribution system demonstrate improved voltage security, effective uncertainty-aware EV coordination, and significantly lower computational complexity than conventional stochastic and robust optimization approaches.

Arash Baharvandi, Duong Tung Nguyen · 0 citations