• 에너지공학
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• 제21권3호
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• pp.301-308
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• 2012

본 논문은 수송용 LPG 수요함수를 추정하고 장 단기 가격 및 소득탄력성을 분석한다. 또한 추정된 장기 가격탄력성을 활용하여 수송용 LPG 소비로 발생하는 소비자 잉여 및 경제적 편익을 추정한다. 분석에 사용된 가격 및 소득 자료는 각각 2003년부터 2012년까지의 소비자 물가지수로 조정한 월별 실질 수송용 LPG 가격과 월별 경기종합지수이다. 수요함수의 추정을 위해 단위근 검정, 공적분 검정, 오차수정모형 추정의 절차를 취했다. 수송용 LPG 수요는 가격에 비탄력적인 것으로 판단되며 단기보다는 장기가 보다 탄력적이다. 수송용 LPG 수요의 장기 가격탄력성은 -0.422였으며, 이를 이용하여 계산된 수송용 LPG 소비의 소비자 잉여와 경제적 가치는 2012년 3월의 경우 각각 9,660억원 및 1조 7,813억원에 달한다.

• 환경영향평가
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• 제24권5호
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• pp.487-498
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• 2015

기온상승과 인구 및 GDP 증가의 영향으로 인해 에너지 문제가 발생하고 있다. 이러한 문제에 대응하기 위해 에너지 수요에 대한 예측이 필요하다. 따라서 본 연구는 에너지 수요관리, 특히 전력부하를 유발하는 냉난방 에너지 수요 관리에 도움이 되고자 가정 부문 냉난방 에너지의 미래 사용량을 예측하고자 한다. 에너지 사용량을 산정하는데 있어 서비스 수요의 산출이 필요하다. 따라서 서비스 수요 산정식을 이용하여 이를 먼저 도출하고, AIM/end-use 모델을 이용하여 에너지 사용량을 산정하였다. 산정 결과 냉난방 서비스 수요는 2010년에 비해 2050년에 모두 증가하는 추세를 보였다. 하지만 에너지 사용량에서 난방은 감소하고, 냉방은 증가하는 것으로 예측되었다.

• Journal of Communications and Networks
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• 제14권6호
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• pp.662-671
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• 2012

Plug-in hybrid electric vehicles (PHEVs) will be widely used in future transportation systems to reduce oil fuel consumption. Therefore, the electrical energy demand will be increased due to the charging of a large number of vehicles. Without intelligent control strategies, the charging process can easily overload the electricity grid at peak hours. In this paper, we consider a smart charging and discharging process for multiple PHEVs in a building's garage to optimize the energy consumption profile of the building. We formulate a centralized optimization problem in which the building controller or planner aims to minimize the square Euclidean distance between the instantaneous energy demand and the average demand of the building by controlling the charging and discharging schedules of PHEVs (or 'users'). The PHEVs' batteries will be charged during low-demand periods and discharged during high-demand periods in order to reduce the peak load of the building. In a decentralized system, we design an energy cost-sharing model and apply a non-cooperative approach to formulate an energy charging and discharging scheduling game, in which the players are the users, their strategies are the battery charging and discharging schedules, and the utility function of each user is defined as the negative total energy payment to the building. Based on the game theory setup, we also propose a distributed algorithm in which each PHEV independently selects its best strategy to maximize the utility function. The PHEVs update the building planner with their energy charging and discharging schedules. We also show that the PHEV owners will have an incentive to participate in the energy charging and discharging game. Simulation results verify that the proposed distributed algorithm will minimize the peak load and the total energy cost simultaneously.

• 전기전자학회논문지
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• 제23권2호
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• pp.380-387
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• 2019

최근 태양열의 열 취득과 태양광의 전기에너지 취득의 상호 보완으로 태양광열(PV/T) 연구가 활발히 진행되고 있으며 대체로 PV/T의 에너지 효율이 더 높은 것으로 연구되고 있다. 본 연구에서는 대상 건물을 모델링하여 연간 에너지 수요량을 산정하고 옥상면에 PV/T를 설치하여 PV/T와 PV의 에너지 생산량을 시뮬레이션별 Case로 비교 분석 하였다. 결과로 남부지방인 부산광역시의 에너지 생산량이 가장 많았다. 또한 에너지 수요량에서 남는 에너지 생산량을 에너지 공유 시스템의 도입을 가정하였을 때 에너지 공급 가능 세대수를 산정한 결과 최대 3.3세대로 나타났다.

• 한국태양에너지학회:학술대회논문집
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• 한국태양에너지학회 2008년도 춘계학술발표대회 논문집
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• pp.304-310
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• 2008

Currently energy use planning council system is mandatory especially for the urban development project planned on a specified scale or more. The goal of existing demand prediction was to calculate the maximum load by multiplying energy load per unit area by building size. The result of this method may be exaggerated and has a limit in the information of period load. The paper suggests a new forecasting process based on standard unit household in order to upgrade the limit in demand prediction method of multi-family housing complex. The new process was verified by comparing actual using amount of multi-family housing complex to forecasting value of energy use plan.

• 한국지열·수열에너지학회논문집
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• 제8권4호
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• pp.1-7
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• 2012

In this study, computer simulation for demand control strategies to save the electric energy and power price in the building central cooling system is done. The demand control and outdoor reset control algorithms are applied by consideration the electric energy and power price according to the energy consumption characteristics. The suggested control methods show better responses in the power price and energy consumption in comparison with the conventional one.

• 한국태양에너지학회:학술대회논문집
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• 한국태양에너지학회 2009년도 춘계학술발표대회 논문집
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• pp.184-189
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• 2009

In order to improve the operation of energy systems, it is necessary for the urban communities to have reliable optimization routines, both computerized and manual, implemented in their organizations. However, before a production plan for the energy system units can be constructed, a prediction of the energy systems first needs to be determined. So, several methodologies have been proposed for energy demand prediction, but due to uncertainties in urban community, many of them will fail in practice. The main topic of this paper has been the development of a method for energy demand prediction at urban community. Energy demand prediction is important input parameters to plan for the energy planing. This paper presents a energy demand prediction method which estimates heat and electricity for various building categories. The method has been based on artificial neural networks(ANN). The advantage of ANN with respect to the other method is their ability of modeling a multivariable problem given by the complex relationships between the variables. Also, the ANN can extract the relationships among these variables by means of learning with training data. In this paper, the ANN have been applied in oder to correlate weather conditions, calendar data, schedules, etc. Space heating, cooling, hot water and HVAC electricity can be predicted using this method. This method can produce 10% of errors hourly load profile from individual building to urban community.

• 설비공학논문집
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• 제25권6호
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• pp.310-316
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• 2013

This study was designed to investigate a method for short-term, real-time energy demand prediction, to cope with changing loads for the effective operation and management of buildings. Through a case study, a novel methodology for real-time energy demand prediction with the use of weather forecasting data was suggested. To perform the input and output operations of weather data, and to calculate solar radiation and EnergyPlus, the BCVTB (Building Control Virtual Test Bed) was designed. Through the BCVTB, energy demand prediction for the next 24 hours was carried out, based on 4 real-time weather data and 2 solar radiation calculations. The weather parameters used in a model equation to calculate solar radiation were sourced from the weather data of the KMA (Korea Meteorological Administration). Depending on the local weather forecast data, the results showed their corresponding predicted values. Thus, this methodology was successfully applicable to anywhere that local weather forecast data is available.

• 대한전기학회논문지:전력기술부문A
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• 제54권8호
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• pp.396-400
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• 2005

This paper proposes the cost analysis on the energy efficient equipment when this equipment is participated in the demand-side bidding. Conventional demand-side bidding is exercised through load re-distribution. However if this load reduction is exercised by the use of high efficient equipment, its effect will be assumed to be more economical. This paper analyses this cost-benefit effect of high efficient equipment in the demand-side bidding.

• Advances in Energy Research
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• 제7권2호
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• pp.85-100
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• 2020

Distributed energy resources (DERs) are essential for coping with growing multiple energy demands. A microgrid (MG) is a small-scale version of the power system which makes possible the integration of DERs as well as achieving maximum demand-side management utilization. Hence, this study focuses on the analysis of optimal power dispatch considering economic aspects in a multi-carrier microgrid (MCMG) with price-responsive loads. This paper proposes a novel time-based demand-side management in order to reshape the load curve, as well as preventing the excessive use of energy in peak hours. In conventional studies, energy consumption is optimized from the perspective of each infrastructure user without considering the interactions. Here, the interaction of energy system infrastructures is considered in the presence of energy storage systems (ESSs), small-scale energy resources (SSERs), and responsive loads. Simulations are performed using GAMS (General Algebraic modeling system) to model MCMG, which are connected to the electricity, natural gas, and district heat networks for supplying multiple energy demands. Results show that the simultaneous operation of various energy carriers, as well as utilization of price-responsive loads, lead to better MCMG performance and decrease operating costs for smart distribution grids. This model is examined on a typical MCMG, and the effectiveness of the proposed model is proven.