Development of AI-Based Adaptive Algorithms for Predictive Control of Hybrid Energy Systems to Maximize Their Thermodynamic and Economic Efficiency

In the context of global energy transformation and the continuous growth of renewable energy sources (RES) in generation portfolios, managing their stochastic behavior and ensuring their integration into existing power systems has become critically important. This study presents a theoretical foundation and proposes AI-based adaptive algorithms for the predictive control of hybrid energy systems (HES). The objective is to formulate a smart control concept aimed at the comprehensive optimization of both thermodynamic and economic performance of HES. Within this framework, a conceptual SAEO (Smart Adaptive Energy Optimization) model is introduced, integrating RES, energy storage technologies (including hydrogen systems), and a gas-steam combined cycle. The results demonstrate that implementation of the developed adaptive algorithms increases overall system efficiency and reduces the levelized cost of energy compared with traditional control schemes based on fixed logic rules. Based on these findings, it is concluded that the intelligent enhancement of control algorithms is a key prerequisite for achieving a synergistic effect in complex hybrid energy systems. The presented results may be of value to power engineers, AI researchers, and strategic planning specialists in the energy sector.