Smart Adaptive Energy Optimization (SAEO): Methodological Foundations and Prospects for Application in Modern Energy Systems

Citation: Petro Bondar, "Smart Adaptive Energy Optimization (SAEO): Methodological Foundations and Prospects for Application in Modern Energy Systems", Universal Library of Multidisciplinary, Volume 02, Issue 02.

Copyright: This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Against the backdrop of the rapid growth of global electricity demand, exacerbated by climate change and accelerated electrification, the energy sector faces a dual challenge: ensuring reliability while simultaneously decarbonizing the system. The handbook systematizes the interdisciplinary foundations of smart adaptive energy optimization (SAEO) as a key approach and establishes an integrated conceptual framework for the design, deployment, and evaluation of SAEO systems. It is demonstrated that SAEO increases the stability of hybrid renewable energy systems, optimizes demand-side management, strengthens predictive maintenance, and improves techno-economic performance indicators. At the same time, barriers to scaling are identified: high computational costs, data infrastructure requirements, and new classes of systemic risks driven by the vulnerability of intelligent algorithms. A viable strategy for the transition to the next generation of intelligent, autonomous, and sustainable energy systems is a holistic, system-integrated approach that treats control, modeling, and security as an inseparable whole. The materials are intended for researchers, power engineers, system architects, and regulators.

Keywords: Smart Adaptive Energy Optimization, Deep Reinforcement Learning, Digital Twins, Cybersecurity, Hybrid Renewable Energy Systems, Demand-Side Management.

https://doi.org/10.70315/uloap.ulmdi.2025.0202006