Recent research:
This body of work integrates advanced optimization, control, and data-driven techniques to enhance power systems with a strong emphasis on renewable energy integration, reliability, and efficiency. Across publications, the researcher advances: (1) bibliometric and foresight analysis of digital innovations in renewable-energy–oriented grids, highlighting AI, IoT, data analytics, and blockchain as key enablers for sustainable infrastructures; (2) contingency-aware distribution restoration using genetic algorithms to maximize restored load while preserving radial topology and voltage limits; (3) short-term economic dispatch in hydro-thermal-hybrid systems under resource constraints, employing Monte Carlo simulations and differential evolution to manage stochastic renewables; (4) investment-oriented market segmentation for distributed generation under regulatory frameworks, using profitability metrics (IRR, BCR) to identify viable residential customers for PV adoption; (5) network reconfiguration, state estimation, and harmonic mitigation through heuristic, metaheuristic, and data-driven models (including DC state estimation with Chi-Square, improved PSO, and unified active power filters) to improve voltage profiles, reduce losses, and meet standards; (6) location and sizing of reactive compensation and energy-storage investments (SVC, BESS) using neural networks and metaheuristics to ensure voltage stability and loss reduction under contingencies; and (7) deployment of intelligent controllers (ANNs, DNNs) for optimal siting of series compensators (SSSC) and reactive devices in transmission networks. The methods consistently combine MATLAB/Simulink, DIgSILENT PowerFactory, MATPOWER, and Monte Carlo simulations, delivering actionable insights for policymakers, planners, and operators. The impact lies in advancing practical, scalable solutions for resilient, low-emission power systems with clear implications for grid modernization, regulatory policy, and investment planning.