Geothermal brine retains considerable thermal energy before reinjection, but its potential for additional power generation is not always fully utilized. Low-temperature solar preheating offers a possible route to increase heat recovery without altering the geothermal source conditions. This study investigates the integration of a field-tested trickle solar collector as a preheater for a small-scale geothermal organic Rankine cycle (ORC), with particular attention to energy performance, exergy efficiency, heat-transfer feasibility, and working-fluid selection. A thermodynamic model was developed using experimental collector data and a geothermal brine stream entering at 188 ℃ and leaving at the 90 ℃ reinjection limit. The brine mass flow rate was fixed at 1.690 kg/s, corresponding to a pinch-feasible 100-kW n-pentane reference cycle. Heat-transfer feasibility was evaluated over the complete counter-current temperature profile using a minimum approach temperature of 10 K. The tested collector produced an average useful heat output of 752.4 W per module and reached a maximum outlet temperature of 51.5 ℃. A field of 88 modules, with a total aperture area of 91.52 m$^2$, supplied 45.024 kW of useful solar heat to the preheater. For the n-pentane cycle, solar preheating increased the net power output from 100 to 106.473 kW while the thermal efficiency remained at 14.377%. The exergy efficiency increased from 51.393% to 53.705%, and the minimum temperature approach remained feasible at 10.120 K. Under the same brine and pinch constraints, R245fa produced the highest net power of 110.961 kW, closely followed by R1233zd(E) at 110.547 kW. Preliminary heat-exchanger sizing yielded a logarithmic mean temperature difference of 12.757 K, a UA value of 3.529 kW/K, and a required heat-transfer area of 7.06–11.76 m$^2$. The results indicate that trickle collectors are better suited to low-temperature preheating than direct ORC evaporation. This integration provides a technically feasible approach to increasing power recovery from geothermal brine while maintaining the original reinjection temperature and thermodynamic operating limits.

Lightweight personal thermal-management systems are attractive for outdoor heat exposure where space cooling is unavailable. A low-power, solar-assisted garment with dual forced-air ventilation was fabricated and tested outdoors on a single day (08:00–16:00; $n$ = 9 nominal hourly observations, eight elapsed one-hour intervals) at ambient temperatures of 32.5–43.1 ℃, relative humidity of 20–34%, and solar irradiance of 420–950 W/m$^2$. The apparent fan-OFF/fan-ON garment-temperature contrast, $\Delta T_{\text{g}}=T_{\text{OFF}}-T_{\text{ON}}$, was positive at all nine observations, with a mean of 3.89 ± 0.97 ℃ and a maximum of 5.0 ℃ at 14:00. Based on the reported combined airflow and $\Delta T_{\text{g}}$, the estimated sensible cooling rate was $\dot{Q}_{\text{s,est}}$ = 38.43–99.52 W (nine-point mean 75.08 W), and the ventilation cooling performance ratio was VCPR = $\dot{Q}_{\text{s, est}}/P_{\text{fan}}$ = 13.72–35.54 (nine-point mean 26.81); these are engineering screening estimates rather than calorimetric measurements or refrigeration coefficient of performance (COP) values. Trapezoidal integration over the eight elapsed one-hour intervals gave a fan-energy demand of 22.40 Wh, a photovoltaic (PV) panels contribution of 13.08 Wh, and an auxiliary-energy requirement of 9.33 Wh. The PV contribution therefore accounted for 58.4% of the fan-energy demand during the test period. Single-day associations with the ambient conditions are reported descriptively, without causal interpretation. The results provide proof-of-concept evidence of a consistent apparent garment-temperature contrast and a quantified partial PV load offset under the hot-dry conditions tested, and they define an 11.19 Wh storage target for autonomous daytime operation.

Hard point-of-interconnection (POI) export limits can strand otherwise-available photovoltaic (PV) energy unless battery storage is dispatched with sufficient anticipation and under physically consistent constraints. This study evaluates a constraint-audited receding-horizon dispatch framework for curtailment recovery at an anonymized 230 MW utility-scale PV plant with a modeled 195.5 MW export cap and a nominal 30 MW/140 MWh battery energy storage system (BESS). The mixed-integer model predictive controller enforces BESS power and energy limits, charge–discharge exclusivity, no grid charging, forecast-dependent discharge headroom, first-action receding-horizon execution, and a common 50%-to-50% terminal-energy protocol. Twelve rolling monthly folds from September 2024 to August 2025 are evaluated against cap-only operation and a protocol-matched perfect-information Oracle. Cap-only operation curtails 26,279 MWh (4.9872% of evaluated PV energy). A transparent 72 h Diurnal model predictive control (MPC) reduces curtailment to 11,719.454 MWh, recovers 14,559.546 MWh, and closes 98.450% of the modeled cap-only-to-Oracle recoverable-energy opportunity; the matched Oracle curtails 11,490.175 MWh. All 3 h closed-loop trajectories satisfy the modeled export cap, BESS limits, terminal equality, charge–discharge exclusivity, discharge-headroom rule, and no-grid-import condition. A matched information-sensitivity analysis then compares History-CatBoost and Weather-CatBoost under the same controller. Weather-CatBoost lowers daylight mean absolute error (MAE) by 2.225 MW at 24 h and 1.175 MW at 72 h relative to History-CatBoost, while annual curtailment is 288.601 MWh higher at 24 h and 99.699 MWh lower at 72 h; the monthly block-bootstrap intervals include zero in both cases. Histogram gradient boosting (HGB) robustness and BESS-sensitivity results further show that the incremental control value of weather information is model- and configuration-dependent. Under the tested plant, cap, BESS, 3 h resolution, horizon, and terminal protocol, physically constrained anticipatory dispatch captures most of the modeled recoverable curtailed energy, leaving comparatively limited residual opportunity for additional forecast sophistication.