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In this study, seven of the most commonly applied covering materials in smart farms are selected as the representative samples for analysis, namely, glass, soft film (polyethylene, PE), soft film (polyolefin, PO), rigid plastic film (ethylene tetra fluoro ethylene, ETFE), rigid plastic sheet (poly methyl methacrylate, PMMA), rigid plastic sheet (polycarbonate, PC double layer), and woven film. For each covering material, visible light transmittance and reflectivity, solar radiation transmittance and reflectivity, thickness, solar heat gain coefficient, and the coefficient of heat transmission are measured according to the test methods in the Korean Industrial Standards (KS) to derive input data for the respective materials. In addition, using the optical and thermal input data as derived above, simulations are performed on the cooling load and daylight characteristics of smart farms to derive basic reference data for the selection of adequate covering materials for the design. Based on the analysis result of the daylight characteristics for each covering material, for a shading rate of 60%, the average values of indoor illuminance were 19,879 lux, 20,012 lux, 19,393 lux, 19,555 lux, 16,560 lux, 16,228 lux, and 11,173 lux for glass, PE film, PO film, ETFE, woven film, PMMA, and PC double layer, respectively, between 6 a.m. and 8 p.m., which correspond to the hours when daylight enters indoor spaces. Considering the target light intensity for strawberry growth at 10,000–40,000 lux, the above results confirm that all the sample covering materials had an indoor illuminance level above the lower limit range of the target light intensity. For the cooling load evaluation, the PC double layer had the lowest value of 482.8 W/m2, and PO film had the highest value of 633.8 W/m2. The difference between the cooling loads of the PC double layer and the PO film is 151 W/m2, which amounts to 23.8%, thus indicating that the selected covering material significantly impacts the cooling load. The cooling load exhibited a pattern similar to that of the coefficient of heat transmission and solar heat gain coefficient, which are key influencing factors for most of the sample materials. However, for PMMA, the cooling load was low because it had a higher coefficient of heat transmission than other materials, but its solar heat gain coefficient was relatively low. Another possible reason for the difference is that the solar heat gain coefficient impacts the cooling load. When the cooling set temperature was controlled from Case 1-1 to Case 1-2, the cooling load decreased by 10.7% on average. In addition, when the cooling set temperature changed from Case 1-1 to Case 1-3, the cooling load decreased by 26.1% on average. For cooling set temperature control, maintaining the temperature around the lower temperature range of the optimal growth temperature of the plants increases the yield, but it also incurs increased cooling load and cost. In terms of cost only, while maintaining the cooling temperature for 24 h at 30 °C, which is the upper limit of the optimal growth temperature, would be advantageous, it will lead to a deterioration of the quality and reductions in yield. Therefore, as follow-up studies for further investigation of the findings of this research, there is the need for an evaluation of the yield and quality with respect to the range of cooling set temperatures. When the internal shading rate was increased to 40% (Case 2-2) with reference to Case 2-1, which was a greenhouse without the application of internal shading, the cooling load decreased by 27.4% on average. Furthermore, when the internal shading rate increased to 50% (Case 2-3) with reference to Case 2-1, the cooling load decreased by 29.3% on average. When the internal shading rate increased to 60% (Case 2-4), the cooling load decreased by 31.5% on average.

Renewable energy generation capacity in Korea is expected to reach about 63.8 GW by 2030 based on calculations using values from a power plan survey (Korea’s renewable energy power generation project plan implemented in September 2017) and the “3020” implementation plan prescribed in the 8th Basic Plan for Long-Term Electricity Supply and Demand that was announced in 2017. In order for the electrical grid to accommodate this capacity, an appropriate power system reinforcement plan is critical. In this paper, a variety of scenarios are constructed involving renewable energy capacity, interconnection measures and reinforcement measures. Based on these scenarios, the impacts of large-scale renewable energy connections on the future power systems are analyzed and a reinforcement plan is proposed based on the system assessment results. First, the scenarios are categorized according to their renewable energy interconnection capacity and electricity supply and demand, from which a database is established. A dynamic model based on inverter-based resources is applied to the scenarios here. The transmission lines, high-voltage direct current and flexible alternating current transmission systems are reinforced to increase the stability and capabilities of the power systems considered here. Reinforcement measures are derived for each stage of renewable penetration based on static and dynamic analysis processes. As a result, when large-scale renewable energy has penetrated some areas in the future in Korean power systems, the most stable systems could be optimally configured by applying interconnection measure two and reinforcement measure two as described here. To verify the performance of the proposed methodology, in this paper, comprehensive tests are performed based on predicted large-scale power systems in 2026 and 2031. Database creation and simulation are performed semi-automatically here using Power System Simulator for Engineering (PSS/E) and Python.