Optimization of Hybrid Renewable Energy Systems: Classical Optimization Methods, Artificial Intelligence, Recent Trend, and Software Tools

Abstract

This article synthesizes the state of the art in the optimization of Hybrid Renewable Energy Systems (HRES), emphasizing that robust HRES planning is inherently an integrated sizing-and-dispatch problem constrained by techno-economic, environmental, and reliability requirements. The review first consolidates classical optimization methods, highlighting the continued relevance of deterministic programming (LP/MILP/MINLP) for transparent and reproducible co-optimization of capacity investment and operational dispatch, alongside analytical, graphical, iterative, and probabilistic approaches for feasibility screening and baseline benchmarking. It then evaluates artificial intelligence–based optimization techniques, including evolutionary computation, swarm intelligence, and multi-objective evolutionary frameworks, noting their effectiveness in nonconvex, mixed-variable, and simulation-driven sizing problems while underscoring the need for rigorous constraint handling, statistical validation, and transparent reporting of computational budgets. The article further examines hybrid optimization strategies that integrate global search with exact dispatch solvers, surrogate-assisted learning, decomposition schemes, and control–co-design paradigms, identifying these as mature approaches that enhance feasibility, scalability, and operational realism. Recent trends in newly proposed AI optimizers are critically discussed, with emphasis on reproducibility, sensitivity analysis, and fair benchmarking against strong baselines. Finally, the article outlines the role of software tools in enabling practical HRES optimization, spanning packaged techno-economic platforms, solver-based modeling environments, and co-simulation workflows for network-constrained planning. Overall, the findings indicate a clear progression toward multi-objective, uncertainty-aware, degradation-informed formulations implemented through integrated toolchains and hybrid solver–AI architectures, with future work warranted on uncertainty quantification, network and resilience constraints, and reproducible evaluation protocols.