Multi-objective optimisation of grid-connected hybrid energy systems: A systematic review and evidence synthesis of cost, reliability, and smart-grid integration

Grid-connected hybrid energy systems (GC-HES) require coordinated optimisation of economic, reliability, environmental and grid-operation objectives, yet published studies remain difficult to compare because system boundaries, objective definitions and statistical reporting are heterogeneous. We therefore applied a record-level data-integrity audit to the 73 bibliography-linked records preserved in the archived screening materials. Eligibility required a 2020–2025 English-language peer-reviewed journal article, an integrated system-level grid- or distribution-connected renewable-generation/storage architecture, explicit multi-objective or multi-criteria optimisation, and quantitative engineering outcomes. The record-level audit retained 17 primary studies and excluded 56 records: 26 secondary or methodological sources, 2 publications outside the protocol window, 3 conference or non-journal papers, 7 records for which grid-connected status was not demonstrated, 12 for which the multi-objective criterion was not demonstrated, and 6 outside the revised hybrid-system scope. Across the 17-study core, PV is represented in all studies, wind in 15, explicit storage in 12, economic or energy-import/loss objectives in 16, reliability or grid-performance objectives in 16, and explicit environmental or renewable-fraction objectives in 9. Nine studies provide direct within-study algorithm comparisons, but heterogeneous objectives, constraints, evaluation budgets and reporting prevent a defensible universal solver ranking. A pooled meta-analysis was not performed because harmonised, source-verifiable arm-level means, dispersion measures and statistical sample sizes were unavailable across the retained studies. The synthesis prioritises reproducible benchmarking, uncertainty-aware optimisation, explicit network constraints, degradation-aware storage, EV/V2G integration, digital twins and validated real-time energy management.