• Journal of Electrical Engineering and Technology
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• 제8권6호
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• pp.1596-1604
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• 2013

Coordinating operation between large-scale wind power and thermal units in multiple time scale is an important problem to keep power balance, especially for the power grids mainly made up of large coal-fired units. The paper proposes a novel operation mode of multi-scale unit commitment (abbr. UC) that includes mid-term UC and day-ahead UC, which can take full advantage of insufficient flexibility and improve wind power accommodation. First, we introduce the concepts of multi-scale UC and then illustrate the benefits of introducing mid-term UC to the wind-coal intensive grid. The paper then formulates the mid-term UC model, proposes operation performance indices and validates the optimal operation mode by simulation cases. Compared with day-ahead UC only, the multi-scale UC mode could reduce the total generation cost and improve the wind power net benefit by decreasing the coal-fired units' on/off operation. The simulation results also show that the maximum total generation benefit should be pursued rather than the wind power utilization rate in wind-coal intensive system.

• Advances in Energy Research
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• 제1권2호
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• pp.127-146
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• 2013

Torrefaction technologies convert assorted biomass feedstocks into energy-concentrated, carbon neutral fuel that is economically transported and easily ground for blending with fossil coals at numerous power plants around the world without needs to retrofit. Utilization of torrefied biomass in conventional electric generating units may be an increasingly attractive alternative for electricity generation as aging power plants in the world need to be upgraded or improved. This paper examines the economic feasibility of torrefaction in different scenarios by modeling torrefaction plants producing 136,078 t/year (150,000 ton/year) biocoal from wood and corn stover. The utilization of biocoal blends in existing coal-fired power plants is modeled to determine the demand for this fuel in the context of emerging policies regulating emissions from coal in the U.S. setting. Opportunities to co-locate torrefaction facilities adjacent to corn ethanol plants and coal-fired power plants are explored as means to improve economics for collaborating businesses. Life cycle analysis was conducted in parallel to this economic study and was used to determine environmental impacts of converting biomass to biocoal for blending in coal-fired power plants as well as the use of substantial flows of off-gasses produced in the torrefaction process. Sensitivity analysis of the financial rates of return of the different businesses has been performed to measure impacts of different factors, whether input prices, output prices, or policy measures that render costs or rewards for the businesses.

• KEPCO Journal on Electric Power and Energy
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• 제6권4호
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• pp.455-460
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• 2020

In order to identify the characteristics of fine particle emissions from thermal power plants, this study conducted measurement of the primary emission concentration of TPM, PM₁₀ and PM_2.5 according to Korea standard test method (ES 01301.1) and ISO 23210 method (KS I ISO 23210). Particulate matters were sampled in total 74 units of power plants such as 59 units of coal-fired power plants, 7 units of heavy oil power plants, 2 units of biomass power plant, and 6 units of liquid natural gas power plants. The average concentration of TPM, PM₁₀, PM_2.5 by fuel are 3.33 mg/m³, 3.01 mg/m³, 2.70 mg/m³ in coal-fired plant, 3.02 mg/m³, 2.99 mg/m³, 2.93 mg/m³ in heavy oil plant, 0.114 mg/m³, 0.046 mg/m³, 0.036 mg/m³ in LNG plant, respectively. These results of TPM, PM₁₀ and PM_2.5 were satisfied with the standards of fine dust emission allowance in all units of power plants, respectively. Also, this study evaluated the characteristics of fine particle emissions by conditions of power plants including generation sources, boiler types and operation years and calculated emission factors and then evaluated fine particle emissions by sources of electricity generation.

• 한국연소학회지
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• 제17권3호
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• pp.30-45
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• 2012

An existing unit of power plant is considered to refurbish it for possible application of carbon capture and storage(CCS). Conceptual design of the plant includes basic considerations on the national and international situation of energy use, environmental concerns, required budget, and time schedule as well as the engineering concept of the plant. While major equipment of the recently upgraded power plant is going to be reused, a new boiler for air-oxy fired dual mode operation is to be designed. Cryogenic air separation unit is considered for optimized capacity, and combustion system accommodates flue gas recirculation with multiple cleaning and humidity removal units. The flue gas is purified for carbon dioxide separation and treatment. This paper presents the background of the project, participants, and industrial background. Proposed concept of the plant operation is discussed for the possible considerations on the engineering designs.

• Journal of Electrical Engineering and Technology
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• 제12권4호
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• pp.1406-1416
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• 2017

In complex and large-scale industries, properly designed fault detection and identification (FDI) systems considerably improve safety, reliability and availability of target processes. In thermal power plants (TPPs), generating units operate under very dangerous conditions; system failures can cause severe loss of life and property. In this paper, we propose a bagged auto-associative kernel regression (AAKR)-based FDI approach for steam boilers in TPPs. AAKR estimates new query vectors by online local modeling, and is suitable for TPPs operating under various load levels. By combining the bagging method, more stable and reliable estimations can be achieved, since the effects of random fluctuations decrease because of ensemble averaging. To validate performance, the proposed method and comparison methods (i.e., a clustering-based method and principal component analysis) are applied to failure data due to water wall tube leakage gathered from a 250 MW coal-fired TPP. Experimental results show that the proposed method fulfills reasonable false alarm rates and, at the same time, achieves better fault detection performance than the comparison methods. After performing fault detection, contribution analysis is carried out to identify fault variables; this helps operators to confirm the types of faults and efficiently take preventive actions.