Ocean Thermal Energy Conversion (OTEC) could provide baseload power for tropical islands and reefs, but its thermal-resource suitability depends on the surface–deep seawater temperature difference (ΔT). This study assessed four South China Sea (SCS) sites—Yongshu Reef, Meiji Reef, Yongxing Island, and Huangyan Island—using monthly Copernicus Marine Environment Monitoring Service (CMEMS) reanalysis data for 2010–2023 within two site-centered averaging windows (±2° and ±0.5°), termed the large and small windows. CMEMS results were validated against in situ profiles from the Argo autonomous profiling-float network, yielding correlation coefficients of 0.66–0.94 and mean absolute errors of 0.44–0.59 °C. Mean ΔT changed little between windows, whereas differences in minimum ΔT ranged from 0.07 to 0.31 °C, with the large window generally producing slightly higher minima and a smoother lower tail. Meiji Reef showed the most stable thermal-resource conditions, whereas Yongxing Island had the highest monthly low-ΔT persistence. At the 22 °C threshold, the persistence index R was 1.429 and 1.339 at Yongxing Island for the large and small windows, respectively, but only 0.006 at Meiji Reef in both windows. Preliminary OTEC screening should therefore consider minimum ΔT and monthly low-ΔT persistence in addition to mean ΔT.

To improve renewable energy utilization and reduce fossil fuel consumption in building heating systems in Northwest China, this study proposes a solar–biogas hybrid heating system for the Jiuquan region. A dynamic simulation model was developed in TRNSYS, and multi-objective optimization was conducted using the NSGA-II algorithm. The optimization objectives were to maximize the solar fraction and minimize the initial system investment. The solar collector area, thermal storage tank volume, and biogas boiler start-up temperature were selected as decision variables. The results showed that the optimized solar fraction increased from 48.98% to 78.40% as the initial investment increased from 40,000 CNY to 80,000 CNY, although the marginal return gradually decreased. The NSGA-II algorithm generated a well-distributed Pareto front, revealing a clear trade-off between economic performance and solar energy utilization. Relative to the author-defined, proportionally scaled reference configurations, the optimized solutions increased the solar fraction by 18–21% across the examined investment levels. These improvement percentages are conditional on the adopted baseline definition. Considering both energy performance and economic feasibility, the scheme with a solar fraction of approximately 70% was identified as the compromise solution. Compared with the initial design, this scheme increased the initial investment by 15.52% and improved the solar fraction by 20.69%. These results indicate that appropriate configuration of key system parameters can substantially enhance renewable energy utilization. The proposed optimization method provides a useful reference for the design and operation of solar–biogas hybrid heating systems in Northwest China.

The building heat demand and the energy system capacity are usually designed independently in solar heating systems for rural houses in cold regions. This leads to oversized system capacity and a lack of collaborative design between the building side and the energy system side. To address these issues, a building–PVT collaborative passive–active design framework was proposed for a typical rural house in Lanzhou, Gansu Province, China. First, a dynamic building thermal model was developed in EnergyPlus. Four typical building configurations, including a baseline house, an insulated house, a sunspace house, and a sunspace house with an intelligent thermal curtain, were established. The hourly heating demand over 8760 h was obtained for each configuration. Then, the hourly building heating demand was used as the unified boundary condition to develop a dynamic PVT heating system model in MATLAB. The responses of the PVT collector area, thermal storage tank volume, and auxiliary heater capacity to building heat demand were analyzed. A CSR was further proposed to establish a quantitative relationship between building heating load reduction and PVT system capacity reduction. Finally, a collaborative multi-objective optimization was carried out using the NSGA-II with the objectives of maximizing the SF and minimizing the LCC. The TOPSIS was adopted to determine the optimal compromise solution. The results show that the integrated passive design combining a sunspace with an intelligent thermal curtain reduces the annual heating energy consumption by 44.0% compared with the baseline house. The reduction in building heat demand simultaneously decreases the required PVT system capacity. The PVT collector area, thermal storage tank volume, and auxiliary heater capacity decrease by 44.14%, 27.55%, and 25.78%, respectively. This indicates that passive building energy-saving measures significantly change the design boundary of the energy system. The proposed CSR effectively quantifies the capacity substitution effect of building-side energy saving on the PVT heating system, and the integrated passive design achieves the highest overall CSR. The multi-objective optimization generates a uniformly distributed Pareto front. The optimal compromise solution achieves an SF of 86.41% with an LCC of 8.34 × 10⁴ CNY. The recommended configuration includes a 50 mm insulation layer, a 40 m² sunspace, a 12.62 m² PVT collector area, and a 1.23 m³ thermal storage tank. Compared with the initial design, the optimized configuration significantly improves solar energy utilization while maintaining a low LCC and further reducing the required auxiliary heater capacity. This study establishes a collaborative analysis framework between building heat demand and PVT system capacity. A quantitative mapping method between building heating load and system capacity is proposed. The framework enables integrated optimization of building thermal design and energy system capacity configuration. The proposed method provides a new theoretical approach and practical guidance for the design of solar heating systems for rural houses in cold regions.