• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.30 no.3
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• pp.130-142
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• 2018

We use heat pumps with thermal storage system to reduce peak usage of electric power during winters and summers. A heat pump stores thermal energy in a thermal storage tank during the night, to meet load requirements during the day. This system stabilizes the supply and demand of electric power; moreover by utilizing the inexpensive midnight electric power, thus making it cost effective. In this study, we propose a system wherein the thermal storage tank and heat pump are modeled using the TRNSYS, whereas the control simulations are performed by (i) conventional control methods (i.e., thermal storage priority method and heat pump priority method); (ii) region control method, which operates at the optimal part load ratio of the heat pump; (iii) load response control method, which minimizes operating cost responding to load; and (iv) dynamic programming method, which runs the system by following the minimum cost path. We observed that the electricity cost using the region control method, load response control approach, and dynamic programing method was lower compared to using conventional control techniques. According to the annual simulation results, the electricity cost utilizing the load response control method is 43% and 4.4% lower than those obtained by the conventional techniques. We can note that the result related to the power cost was similar to that obtained by the dynamic programming method based on the load prediction. We can, therefore, conclude that the load response control method turned out to be more advantageous when compared to the conventional techniques regarding power consumption and electricity costs.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2017.04a
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• pp.155-155
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• 2017

최근 애플망고 스마트 농가에 에너지 사용량이 증가됨에 따라 에너지 절감을 위한 대책들과 화석연료를 대체하는 다양한 신재생에너지 도입에 대한 요구들이 늘고있다. 본 연구에서는 애플망고 스마트 농가에 여러 에너지원들을 혼합하여 사용할 수 있도록 실증시험 모델을 구축하고 운영함으로써 그 효용성을 검토하고자 하였다. 우선 애플망고 특성을 고려한 비닐온실의 최대 냉난방부하량과 에너지모델을 분석하여 신재생 에너지원들의 혼합 및 기존 공조설비와의 연계를 계산하였다. 애플망고 시험 농지로는 재배에 적합한 제주도 서귀포를 선정하였으며, 기존의 경유 난방기를 사용하는 비교시험 하우스, 기존의 경유와 태양광, 지하 공기 히트펌프 난방기를 혼합하여 사용하는 실증시험 하우스, 경유와 지하공기 히트펌프 난방기를 사용하는 대조시험 하우스를 10~11월 두 달간 운영하여 그 결과들을 평가하였다. 온실 내외부에 온도, 습도, CO2를 측정할 수 있는 6점의 센서부들을 설치하였고, 적산 전력계와 유량계를 설치하여 데이터를 수집하였으며, 모든 시험 데이터는 모바일 원격으로 제어 및 모니터링이 가능하도록 구성하였다. 시험 결과, 각 하우스들에서 수확한 과실의 수량과 품질은 유사하게 평가되었지만, 실증시험 하우스의 난방비가 비교시험 하우스보다 절감되었다. 하지만 실증시험 하우스의 경우 높은 시설유지비로 인해 이를 고려한 사용료는 비교시험 하우스보다 더 비싸게 평가되었다. 본 연구를 통해 생산된 잉여전력을 매전할 때 이로 인한 이용비는 비교시험 하우스보다 더 경제적임을 확인할 수 있었다. 또한 기존의 경유와 지하공기 히트펌프 난방기를 혼합한 대조시험 하우스의 난방비용이 경제성에서 더 유리함을 알 수 있었다. 따라서 본 연구를 통해 애플망고 스마트 농가에 적합한 에너지-믹스 모델을 구축할 수 있었으며, 다양한 신재생에너지들의 효용성들을 검토할 수 있었다.

• Journal of Bio-Environment Control
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• v.27 no.2
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• pp.166-172
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• 2018

This study set out to conduct a field survey with smart greenhouse-based farms in seven types to figure out the actual state of smart greenhouses distributed across the nation before selecting a system to implement an optimal greenhouse environment and doing a research on higher productivity based on data related to crop growth, development, and environment. The findings show that the farms were close to an intelligent or advanced smart farm, given the main purposes of leading cases across the smart farm types found in the field. As for the age of farmers, those who were in their forties and sixties accounted for the biggest percentage, but those who were in their fifties or younger ran 21 farms that accounted for approximately 70.0%. The biggest number of farmers had a cultivation career of ten years or less. As for the greenhouse type, the 1-2W type accounted for 50.0%, and the multispan type accounted for 80.0% at 24 farms. As for crops they cultivated, only three farms cultivated flowers with the remaining farms growing only fruit vegetables, of which the tomato and paprika accounted for approximately 63.6%. As for control systems, approximately 77.4% (24 farms) used a domestic control system. As for the control method of a control system, three farms regulated temperature and humidity only with a control panel with the remaining farms adopting a digital control method to combine a panel with a computer. There were total nine environmental factors to measure and control including temperature. While all the surveyed farms measured temperature, the number of farms installing a ventilation or air flow fan or measuring the concentration of carbon dioxide was relatively small. As for a heating system, 46.7% of the farms used an electric boiler. In addition, hot water boilers, heat pumps, and lamp oil boilers were used. As for investment into a control system, there was a difference in the investment scale among the farms from 10 million won to 100 million won. As for difficulties with greenhouse management, the farmers complained about difficulties with using a smart phone and digital control system due to their old age and the utter absence of education and materials about smart greenhouse management. Those difficulties were followed by high fees paid to a consultant and system malfunction in the order.