Hybrid Renewable Energy Systems are pivotal for the sustainable energy transition, yet their design and operation present complex optimization challenges due to diverse components, stochastic resources, and multifaceted objectives. This systematic review formalizes the HRES optimization problem space and identifies critical research gaps. Employing the PRISMA 2020 guidelines, it comprehensively analyzes the literature (2015–2025) from Scopus, IEEE Xplore, and Web of Science, focusing on architectures, mathematical formulations, objectives, and solution methodologies. The results reveal a decisive shift from single-objective to multi-objective optimization (MOO), increasingly incorporating environmental and emerging social criteria alongside traditional economic and technical goals. Metaheuristic algorithms (e.g., NSGA-II, MOPSO) and AI techniques dominate solution strategies, though challenges persist in scalability, uncertainty management, and real-time control. The integration of hydrogen storage, vehicle-to-grid (V2G) technology, and multi-vector energy systems expands system boundaries. Key gaps include the lack of holistic frameworks co-optimizing techno-economic, environmental, social, and resilience objectives; disconnect between long-term planning and short-term operation; computational limitations for large-scale or real-time applications; explainability of AI-based controllers; high-fidelity degradation modeling for emerging technologies; and bridging the “valley of death” between simulation and bankable deployment. Future research must prioritize interdisciplinary collaboration, standardized social/resilience metrics, scalable and trustworthy AI, and validation frameworks to unlock HRESs’ potential.

Europe faces sustained demographic contraction—sub-replacement fertility and aging—and Ukraine, whose full-scale war triggered Europe’s largest forced-displacement episode, is its sharpest case. We treat demographic security as a measurable state and construct a composite Demographic Security Index from nine indicators across five dimensions (natural change, aging, migration, health, and gender balance), measured identically for the EU-27 and Ukraine, 2014–2024. Construction uses pooled min–max normalization and equal-indicator weighting—so the volatile migration indicator receives one-ninth weight and cannot dominate the composite—with leave-one-out, random-weight, and alternative-scheme sensitivity checks, winsorized migration, and fixed-effects regressions on income. Ukraine ranks 26th–28th during 2014–2021 and reaches the panel’s lowest score, 0.282, in 2022, with the fall concentrated in the migration, gender-balance, and natural-change dimensions. Its limited 2024 uptick to 27th is driven by one projected migration input; removing the migration dimension leaves Ukraine last. Across countries, demographic security and income co-occur strongly (r ≈ 0.81); within countries, the short-run association is weakly negative, shared between the migration and gender-balance channels. Demographic security is thus a structural correlate of the economic dimension of development, not a short-run driver, and crisis-era composites should be read by dimension, not their headline score.