AI-Assisted Power Management in Renewable Grids: DC– DC Converter Optimization and Predictive Load Balancing for Sustainable Energy Systems

Abstract

The transition to sustainable energy systems is accelerating, driven by concerns over climate change, energy security, and the need for
decarbonization. Renewable energy sources (RES) such as solar and wind are intermittent and variable, posing considerable challenges
for grid stability, efficient power conversion, and load and supply balancing. In particular, DC‐DC converters are critical components in
renewable energy integration, enabling efficient voltage regulation, maximum power point tracking (MPPT), and interfacing between
generation, storage, and load. However, conventional control approaches (fixed‐duty cycle, simple MPPT algorithms, PI/PID controllers)
struggle under large fluctuations in irradiance, temperature, load, and energy demand. Predictive load balancing and AI‐based control
promise to improve efficiency, stability, and reliability in renewable grids.
This paper proposes an integrated framework for AIassisted power management in renewable grids, focusing on two main aspects:
(i) optimization of DCDC converters (topology, control parameters, MPPT, voltage regulation) using AI (neural networks, predictive
control, supervised & possibly reinforcement learning); (ii) predictive load balancing using forecasting methods for demand, supply,
and storage behaviour to schedule loads, manage storage charging/discharging, and reduce mismatches. The framework includes
real‐time monitoring of generation sources (solar, wind, etc.), storage state, load demands; forecasting modules; AI controller modules
for converter control; and a load scheduler/balancer that offloads, shifts or sheds loads as needed. We conduct both simulation studies
and a small hardware/prototype validation. Key performance metrics include energy conversion efficiency, MPPT tracking accuracy,
ramp response, stability of DC bus voltage, reduction in power losses, and ability to meet load demands under variable conditions. For
load balancing we measure forecast accuracy, demand‐supply mismatch reduction, peak load shaving, and storage utilization.
Our results indicate that AI‐based MPPT and converter optimization can increase energy conversion efficiency by ~1020% over
conventional MPPT/PI control in typical PV + storage systems under variable irradiance and temperature. Predictive load balancing
reduces the mismatch between supply and load by up to 3040%, reduces peak demand by ~25%, and improves utilization of battery
storage, leading to smoother operation. The combined approach yields a more stable DC‐bus voltage, fewer voltage dips and transients
under load changes, and better overall grid reliability. We also report on trade‐offs: the AI modules introduce computational overhead
and may require higher cost hardware; forecasting errors can degrade performance; overfitting or model drift in AI controllers under
new environmental conditions. Nonetheless, we conclude that AIassisted DCDC converter optimization + predictive load balancing is
a promising route toward sustainable, efficient, and resilient renewable grids.