• KIEAE Journal
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• v.13 no.3
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• pp.91-96
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• 2013

The purpose of this study was to analyze the annual energy demand including heating, cooling and lighting according to kind of windows with transparent thin-film a-Si Building Integrated Photovoltaic(a-Si BIPV) for office building. The analysis results of the annual energy demand indicated that the a-si BIPV window was reduced by 8.4% than the clear gazing window. The base model A was combinate with a-Si BIPV window area of 67% and clear window area of 33% among the total exterior area. The model B is to be applied with low-e clear glass instead of clear glass of the base model A. The model B was reduced to annual energy demand of 1% more than the model A. Therefore, By using a-si BIPV solar module, the cooling energy demand can be reduced by 53%(3.4MWh) and the heating energy demand can be increase by 58%(2.4MWh) than clear glazing window in office building. Also, Model C applied to the high efficient lighting device to the model B was reduced to annual energy demand of 14.4% more than the Model D applied to the high efficient lighting device to the model A. The Model E applied with daylight dimming control system to the Model C was reduced to annual energy demand of 5.9% more than Model C.

• Journal of Electrical Engineering and Technology
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• v.12 no.4
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• pp.1511-1518
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• 2017

Renewable energy hybrid systems look into the process of choosing the finest arrangement of components and their sizing with suitable operation approach to deliver effective, consistent and cost effective energy source. This paper presents hybrid renewable energy system (HRES) solar photovoltaic, downdraft biomass gasifier, and fuel cell based generation system. HRES electrical power to supply the electrical load demand of academic research building sited in $23^{\circ}12^{\prime}N$ latitude and $77^{\circ}24^{\prime}E$ longitude, India. Fuzzy logic programming discover the most effective capital and replacement value on components of HRES. The cause regarding fuzzy logic rule usage on HOMER pro (Hybrid optimization model for multiple energy resources) software program finds the optimum performance of HRES. HRES is designed as well as simulated to average energy demand 56.52 kWh/day with a peak energy demand 4.4 kW. The results shows the fuel cell and battery bank are the most significant modules of the HRES to meet load demand at late night and early morning hours. The total power generation of HRES is 23,794 kWh/year to the supply of the load demand is 20,631 kWh/year with 0% capacity shortage.

• Structural Engineering and Mechanics
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• v.63 no.4
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• pp.521-536
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• 2017

Reinforced concrete core-wall structures with buckling-restrained brace outriggers are interesting systems which have the ability to absorb and dissipate energy during strong earthquakes. Outriggers can change the energy demand in a tall building. In this paper, the energy demand was studied by using the nonlinear time history analysis for the mentioned systems. First, the structures were designed according to the prescriptive codes. In the dynamic analysis, three approaches for the core-wall were investigated: single plastic hinge (SPH), three plastic hinge (TPH) and extended plastic hinge (EPH). For SPH approach, only one plastic hinge is allowed at the core-wall base. For TPH approach, three plastic hinges are allowed, one at the base and two others at the upper levels. For EPH approach, the plasticity can extend anywhere in the wall. The kinetic, elastic strain, inelastic and damping energy demand subjected to forward directivity near-fault and ordinary far-fault earthquakes were studied. In SPH approach for all near-fault and far-fault events, on average, more than 65 percent of inelastic energy is absorbed by buckling-restrained braces in outrigger. While in TPH and EPH approaches, outrigger contribution to inelastic energy demand is reduced. The contribution of outrigger to inelastic energy absorption for the TPH and EPH approaches does not differ significantly. The values are approximately 25 and 30 percent, respectively.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.9
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• pp.1799-1807
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• 2016

In this paper, we propose the automatic demand response systems which reduce the electric power consumption for the period automatically distinct from the existing passive demand response that a subscriber directly controls the energy consumption. The proposed systems are based on SEP 2.0 and consist of the demand response management program, the demand response server, and the demand response client. The demand response program shows the current status of the electric power use to a subscriber and supports the function which the administrator enables to creates or cancels a demand response event. The demand response server transmits the demand response event received from the demand response management program to the demand response client through SEP 2.0 protocol, and it stores the metering data from the demand response client in a database. After extracting the data, such as the demand response the start time, the duration, the reduction level, the demand response client reduces the electric power consumption for the period.

• Journal of Integrative Natural Science
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• v.12 no.1
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• pp.20-23
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• 2019

Demand response is usually operated through using the power rates and incentives. Demand management based on power charges is the most rational and efficient demand management method, and such methods include rolling base charges with peak time, sliding scaling charges depending on time, sliding scaling charges depending on seasons, and nighttime power charges. Search for other methods to stimulate resources on demand by actively deriving the demand reaction of loads to increase the energy efficiency of loads. In this paper, ESS algorithm for saving energy based on predicting the amount of solar power generation that can be used for buildings with small loads not under electrical grid.

• Journal of Electrical Engineering and Technology
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• v.10 no.1
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• pp.64-74
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• 2015

Because of the burgeoning demand of the energy, the countries are finding sustainable solutions for these emerging challenges. Demand Side Management is playing a significant role in managing the demand with an aim to support the electrical grid during the peak hours. However, advancement in controls and communication technologies, the aggregators are appearing as a third party entity in implementing demand response program. In this paper, a detailed mathematical framework is discussed in which the aggregator acts as an energy service provider between the utility and the consumers, and facilitate the consumers to actively participate in demand side management by introducing the new concept of demand reduction bidding (DRB) under constrained direct load control. Paper also presented an algorithm for the proposed framework and demonstrated the efficacy of the algorithm by considering few case studies and concluded with simulation results and discussions.

• Structural Engineering and Mechanics
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• v.23 no.2
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• pp.177-193
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• 2006

Determining the hysteretic energy demand and dissipation capacity and level of damage of the structure to a predefined earthquake ground motion is a highly non-linear problem and is one of the questions involved in predicting the structure's response for low-performance levels (life safe, near collapse, collapse) in performance-based earthquake resistant design. Neural Network (NN) analysis offers an alternative approach for investigation of non-linear relationships in engineering problems. The results of NN yield a more realistic and accurate prediction. A NN model can help the engineer to predict the seismic performance of the structure and to design the structural elements, even when there is not adequate information at the early stages of the design process. The principal aim of this study is to develop and test multi-layered feedforward NNs trained with the back-propagation algorithm to model the non-linear relationship between the structural and ground motion parameters and the hysteretic energy demand in steel moment resisting frames. The approach adapted in this study was shown to be capable of providing accurate estimates of hysteretic energy demand by using the six design parameters.

• Journal of Electrical Engineering and Technology
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• v.11 no.2
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• pp.300-308
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• 2016

Application of demand-side resources to automatic generation control (AGC) has a great significance for improving the dynamic control performance of power system frequency regulation. This paper investigates the possibility of providing regulation services by demand-side energy storage in electric vehicle battery swapping stations (BSS). An interaction framework, namely station-to-grid (S2G), is presented to integrate BSS energy storage into power grid for giving benefits to frequency regulation. The BSS can be regarded as a lumped battery energy storage station through S2G framework. A supplementary AGC method using demand-side BSS energy storage is developed considering the vehicle user demand of battery swapping. The effects to the AGC performance are evaluated through simulations by using a two-area interconnected power grid model with step and random load disturbance. The results show that the demand-side BSS can significantly suppress the frequency deviation and tie-line power fluctuations.

• Transactions of the Korean hydrogen and new energy society
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• v.33 no.5
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• pp.591-597
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• 2022

Recently, technologies for efficient power grid operation have become important due to climate change. For this reason, predicting power demand using deep learning is being considered, and it is necessary to understand the influence of characteristics of each region, industrial structure, and climate. This study analyzed the power demand of New Jersey in US, with a high urbanization rate and a large service industry, and West Virginia in US, a low urbanization rate and a large coal, energy, and chemical industries. Using recurrent neural network algorithm, the power demand from January 2020 to August 2022 was learned, and the daily and weekly power demand was predicted. In addition, the power grid operation based on the power demand forecast was discussed. Unlike previous studies that have focused on the deep learning algorithm itself, this study analyzes the regional power demand characteristics and deep learning algorithm application, and power grid operation strategy.

• Environmental and Resource Economics Review
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• v.17 no.3
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• pp.1-29
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• 2008

The goal of this study is to analyze the effects of the second energy tax reform of the transportation sector in Korea. For this purpose, we estimated the elasticities of energy demand(for gasoline, diesel and LPG) by using the ARDL(Auto-Regressive Distributed Lag) Model during the period of 1997 and 2005. We have the empirical results that the demand for diesel would decrease as much as of 382 million barrel per year and the demand for LPG would increase as much as of 20 million barrel per year since 2007. The second energy tax reform would also result in the decrease of 27,346 ton of air pollutants and 0.96 million ton of carbon dioxide per year. This shows that the second energy tax reform would have achieved its own policy goals by reducing energy demand and improving the quality of environment.