Recent research:
This research portfolio consolidates a strong focus on power systems, renewable energy and microgrids, with extensive use of optimization, control, and data-driven methods to enhance reliability, efficiency, and planning of electrical networks. The work demonstrates practical solutions for damping subsynchronous resonance with fuzzy-PI STATCOM in DFIG-based networks, robust and scalable generation–transmission planning under high renewable penetration, and hydrothermal dispatch incorporating valve-point effects. It also covers reactive power optimization, DC grid efficiency, optimal placement of energy storage and distributed generation, network reconfiguration, and state estimation using statistical and Kalman-filter-based approaches. Across these publications, methodologies include metaheuristics (BAT, ABC, PSO, ALO), differential evolution, MVMO, Dijkstra-inspired scheduling, genetic algorithms, and MPPT for PV pumping, all validated on standard IEEE test systems and real-world-like models. The impact lies in providing cost reductions, improved voltage stability and quality, reduced losses, enhanced reliability, and scalable frameworks for planning and operation amidst increasing renewable integration and grid complexity. The narrative emphasizes practical applicability (MATPOWER/DIgSILENT, EES, MATLAB) and the integration of stochastic considerations (Monte Carlo, uncertainty, reliability criteria) with deterministic and heuristic optimization to inform decision-making in modern grids.