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Nathaly Verónica Orozco Garzón

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Open access Jul 2026

Capability-Curve-Constrained Optimal Reactive Power Flow for Renewable-Integrated Transmission Systems

Optimal reactive power flow (ORPF) is a steady-state optimization problem used to determine reactive-power-related control settings in AC power systems while satisfying network operating constraints. In renewable-integrated transmission systems, explicitly representing the feasible reactive-power contribution of inverter-interfaced resources is essential, because wind, photovoltaic (PV), and battery energy storage system (BESS) units cannot operate as unlimited or overly flexible reactive-power sources. Their reactive capability depends on active-power output, apparent-power rating, voltage conditions, and equipment-level capability curves. This paper evaluates the impact of including these capability curves in the solution of the ORPF problem. A MATLAB-DIgSILENT PowerFactory co-simulation framework is implemented, in which MATLAB applies a hybrid particle swarm optimization-pattern search procedure and DIgSILENT PowerFactory performs repeated AC power-flow evaluations using detailed network models and predefined capability limits. The framework is tested on modified IEEE 39-bus and IEEE 118-bus systems with wind, PV, and BESS resources. The results show that neglecting capability curves can produce unrealistic reactive-power allocations for inverter-based units, whereas enforcing these limits shifts the ORPF solution toward operating points consistent with the modeled equipment capability. The study demonstrates the importance of capability-curve representation for obtaining physically meaningful steady-state ORPF results and supports clearer comparison of constrained and unconstrained dispatch cases.

José Oscullo Lala, Nathaly Verónica Orozco Garzón, Henry Ramiro Carvajal Mora et al. · 0 citations
Open access Jul 2026

Distributed Antenna Array and RIS-Assisted Planning Framework for Intelligent Coverage Optimization in B5G/6G Cell-Free Massive MIMO

The transition to Beyond fifth generation of wireless networks (B5G) and sixth generation of wireless networks (6G) exposes the severe interference and coverage limitations of conventional cell-centric architectures. To overcome these bottlenecks, this paper presents a scalable four-layer radio network planning framework that jointly optimizes the deployment of distributed active antenna arrays and passive reconfigurable intelligent surfaces (RISs). The proposed framework integrates a digital twin (DT) loop within an Open-RAN (O-RAN) architecture, employing multi-agent deep reinforcement learning (MADRL) and fractional programming (FP) for real-time joint active and passive beamforming optimization. Extensive Monte Carlo simulations in a dense urban environment demonstrate a 45% increase in spectral efficiency, a 30% reduction in uplink interference, and an 84% reduction in coverage holes compared to legacy 5G networks. Ultimately, these results provide network operators with a cost-effective, standards-compliant blueprint to extend non-line-of-sight (NLOS) coverage by 40% without incurring the prohibitive capital expenditure (CAPEX) of dense active hardware deployments. Furthermore, the proposed architecture demonstrates a competitive 10–15% margin of improvement in spectral efficiency over recent state-of-the-art DRL-based RIS frameworks.

Valdemar Farré, J. Vega-Sánchez, Alejandro Cama-Pinto et al. · 0 citations