Deep networks dominate photovoltaic (PV) thermographic fault detection but need large annotated datasets and resist interpretation. We present a lightweight, interpretable infrared workflow combining support-vector-machine (SVM) module/background segmentation from four handcrafted features with an adaptive grid analysis labelling regions as nominal-intensity, high-intensity anomaly or low-intensity anomaly relative to a module-internal reference; the anomaly classes are inspection candidates, not confirmed faults. Evaluation used 21 close-range images of one 20 W module—recorded with the camera’s visible-light edge fusion active, so they are fused infrared/visible frames—and all 596 of a public five-sector UAV dataset. Segmentation against manual masks reached a mean intersection-over-union of 0.64; a feature ablation shows intensity statistics dominate, and an end-to-end Otsu pipeline gives almost the same high-intensity share (4.54% versus 4.50%): the SVM contributes reproducibility—removing the manual segmentation threshold, though not the empirical +48/−60 offsets—not accuracy. High-intensity regions concentrated in the module’s lower half, co-locating with a bus-bar defect known from hardware inspection—suggestive, not validated. The single-module, image-level close-range evaluation is optimistic, and the UAV shares, from a separately trained SVM, illustrate cross-domain application only. Segmentation runs at about 15 images per second on CPU. The method is a relative-intensity thermal screening workflow, not a validated defect-diagnosis or plant-health assessment method, and applies only where acquisition is controlled and the offsets are recalibrated for the target camera and palette.

Battery energy storage systems (BESSs) are increasingly coupled with photovoltaic (PV) generation, yet their deployment is governed by economics rather than by technical feasibility. This review synthesises 122 indexed Q1/Q2 studies (2016–2026) on the financial payback and return on investment of PV-coupled storage, extending the analysis to vehicle-to-grid (V2G) integration. Records retrieved from Scopus, Web of Science, IEEE Xplore, and the MDPI portal were screened to peer-reviewed journals, assigned to ten thematic clusters, appraised against a ten-criterion reporting-transparency rubric, and combined by narrative synthesis. Reported payback periods range from a few years to beyond the asset’s service life, and the levelized cost of storage spans roughly 170–350 USD/MWh. In the reviewed corpus, the retail-to-export price spread, the stacking of self-consumption, arbitrage, grid-service revenues, and degradation-aware operation each move returns in a consistent direction, whereas which of them binds hardest is a property of the case rather than a ranking the evidence supports. V2G is almost always analysed in isolation from stationary storage; an illustrative harmonised comparison indicates that it substitutes for stationary capacity rather than adding to it. The review maps the combined PV+BESS+V2G revenue stack and identifies an integrated, degradation-corrected, policy-sensitive economic model as the principal research gap.