• Title/Summary/Keyword: Annual electricity generation

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Analysis of Energy Savings and CO2 Emission Reductions via Application of Smart Grid System (지능형 전력망(스마트 그리드) 적용을 통한 에너지 절감 및 CO2 감축 효과 분석)

  • Park, Soo-Hwan;Han, Sang-Jun;Wee, Jung-Ho
    • Journal of Korean Society of Environmental Engineers
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    • v.39 no.6
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    • pp.356-370
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    • 2017
  • The energy savings and $CO_2$ emission reductions obtainable from the situation that the Smart Grid system (SGs) is assumed to be applied in Korea up to 2030 is quantitatively analyzed with many reported data. For calculation, SGs is divided into five sectors such as Smart Transmission and Distribution (ST&D), Smart Consumer (SC), Smart Electricity Service (SES), Smart Renewable Energy (SRE) and Smart Transportation (ST). Total annual energy savings in 2030 is estimated to be approximately 103,121 GWh and this is 13.1% of total electricity consumption outlook. Based on this value, total amount of reducible $CO_2$ emissions is calculated to 55.38 million $tCO_2$, which is 17.6% of total nation's GHG reduction target. Although the contribution of energy saving due to SGs to total electricity consumption increases as years go by, that of $CO_2$ emission reduction gradually decreases. This might be because that coal fired based power generation is planned to be sharply increased and the rate of $CO_2$ emission reduction scheduled by nation is very fast. The contributable portion of five each sector to total $CO_2$ emission reductions in 2030 is estimated to be 44.37% for SC, 29.16% for SRE, 20.12% for SES, 5.11% for ST&D, and 1.24% for ST.

Process Simulation and Economic Feasibility of Upgraded Biooil Production Plant from Sawdust (톱밥으로부터 생산되는 개질 바이오오일 생산공장의 공정모사 및 경제성 분석)

  • Oh, Chang-Ho;Lim, Young-Il
    • Korean Chemical Engineering Research
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    • v.56 no.4
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    • pp.496-523
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    • 2018
  • The objective of this study is to evaluate the economic feasibility of two fast pyrolysis and biooil upgrading (FPBU) plants including feed drying, fast pyrolysis by fluidized-bed, biooil recovery, hydro-processing for biooil upgrading, electricity generation, and wastewater treatment. The two FPBU plants are Case 1 of an FPBU plant with steam methane reforming (SMR) for $H_2$ generation (FPBU-HG, 20% yield), and Case 2 of an FPBU with external $H_2$ supply (FPBUEH, 25% yield). The process flow diagrams (PFDs) for the two plants were constructed, and the mass and energy balances were calculated, using a commercial process simulator (ASPEN Plus). A four-level economic potential approach (4-level EP) was used for techno-economic analysis (TEA) under the assumption of sawdust 100 t//d containing 40% water, 30% equity, capital expenditure equal to the equity, $H_2$ price of $1050/ton, and hydrocarbon yield from dried sawdust equal to 20 and 25 % for Case 1 and 2, respectively. TCI (total capital investment), TPC (total production cost), ASR (annual sales revenue), and MFSP (minimum fuel selling price) of Case 1 were $22.2 million, $3.98 million/yr, $4.64 million/yr, and $1.56/l, respectively. Those of Case 2 were $16.1 million, $5.20 million/yr, $5.55 million/yr, and $1.18/l, respectively. Both ROI (return on investment) and PBP (payback period) of Case 1(FPBU-HG) and Case 2(FPBU-EH) were the almost same. If the plant capacity increases into 1,500 t/d for Case 1 and Case 2, ROI would be improved into 15%/yr.

A Study on the Evaluation of Potential Hydro-electric Power in North Korea (북한의 수력발전가능량 산정 및 평가에 대한 연구)

  • Park, Miri;Ahn, Jaehyun
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.38 no.1
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    • pp.41-49
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    • 2018
  • This study is to analyze and evaluate water resource development potential in North Korea. The study was conducted to analyze selected potential hydropower as an indicator to evaluate water resource development potential. Potential hydropower means theoretical value about the potential capacity of river. It is used to evaluate the amount of development through the hydropower generation. For calculating potential hydropower, monthly average and annual average of rainfall for each river basin were calculated by using the data of 27 rainfall stations in North Korea. As a result of the calculation of theoretical potential hydropower by rainfall in the seven major basins in North Korea, the Aprok River basin was analyzed to be the largest with $7,562.2{\times}10^3kW$. The efficiency and utilization rate of theoretical potential hydraulic power in South Korea and North Korea was 42.3% and 36.2%, respectively. The Daedong River basin's potential hydropower utilization rate is 12.3%, which is the lowest in North Korea. In the case of Daedong River basin, more than 40% of the total population is inhabited, so demand for water and electricity is expected to be the largest. Therefore, the Daedong River basin is considered as a priority area for water resource development. The results of this study are expected to be used as basic data for future water resource development projects and research activities in North Korea.

LNG Gas Demand Forecasting in Incheon Port based on Data: Comparing Time Series Analysis and Artificial Neural Network (데이터 기반 인천항 LNG 수요예측 모형 개발: 시계열분석 및 인공신경망 모형 비교연구)

  • Beom-Soo Kim;Kwang-Sup Shin
    • The Journal of Bigdata
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    • v.8 no.2
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    • pp.165-175
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    • 2023
  • LNG is a representative imported cargo at Incheon Port and has a relatively high contribution to the increase/decrease in overall cargo volume at Incheon Port. In addition, in the view point of nationwide, LNG is the one of the most important key resource to supply the gas and generate electricity. Thus, it is very essential to identify the factors that have impact on the demand fluctuation and build the appropriate forecasting model, which present the basic information to make balance between supply and demand of LNG and establish the plan for power generation. In this study, different to previous research based on macroscopic annual data, the weekly demand of LNG is converted from the cargo volume unloaded by LNG carriers. We have identified the periodicity and correlations among internal and external factors of demand variability. We have identified the input factors for predicting the LNG demand such as seasonality of weekly cargo volume, the peak power demand, and the reserved capacity of power supply. In addition, in order to predict LNG demand, considering the characteristics of the data, time series prediction with weekly LNG cargo volume as a dependent variable and prediction through an artificial neural network model were made, the suitability of the predictions was verified, and the optimal model was established through error comparison between performance and estimates.