• 경영과학
• /
• 제33권1호
• /
• pp.35-47
• /
• 2016

Generation of electricity using wind power has received considerable attention worldwide in recent years mainly due to its minimal environmental impact. However, volatility of wind power production causes additional problems to provide reliable electricity to an electrical grid regarding power system operations, power system planning, and wind farm operations. Those problems require appropriate stochastic models for the electricity generation output of wind power. In this study, we review previous literatures for developing the stochastic model for the wind power generation, and propose a systematic procedure for developing a stochastic model. This procedure shows a way to build an ARIMA model of volatile wind power generation using historical data, and we suggest some important considerations. In addition, we apply this procedure into a case study for a wind farm in the Republic of Korea, Shinan wind farm, and shows that our proposed model is helpful for capturing the volatility of wind power generation.

• Journal of Construction Engineering and Project Management
• /
• 제3권3호
• /
• pp.23-34
• /
• 2013

As climate change and environmental pollution become one of the biggest global issues today, new renewable energy, especially solar photovoltaic (PV) system, is getting great attention as a sustainable energy source. However, initial investment cost of PV system is considerable, and thus, it is crucial to predict electricity generation accurately before installation of the system. This study analyzes the loss ratio of solar photovoltaic electricity generation from the actual PV system monitoring data to predict electricity generation more accurately in advance. This study is carried out with the following five steps: (i) Data collection of actual electricity generation from PV system and the related information; (ii) Calculation of simulation-based electricity generation; (iii) Comparative analysis between actual electricity generation and simulation-based electricity generation based on the seasonality; (iv) Stochastic approach by defining probability distribution of loss ratio between actual electricity generation and simulation-based electricity generation ; and (v) Case study by conducting Monte-Carlo Simulation (MCS) based on the probability distribution function of loss ratio. The results of this study could be used (i) to estimate electricity generation from PV system more accurately before installation of the system, (ii) to establish the optimal maintenance strategy for the different application fields and the different season, and (iii) to conduct feasibility study on investment at the level of life cycle.

• 국제학술발표논문집
• /
• The 5th International Conference on Construction Engineering and Project Management
• /
• pp.375-385
• /
• 2013

As climate change and environmental pollution become one of the biggest global issues today, new renewable energy, especially solar photovoltaic (PV) system, is getting great attention as a sustainable energy source. However, initial investment cost of PV system is considerable, and thus, it is crucial to predict electricity generation accurately before installation of the system. This study analyzes the loss ratio of solar photovoltaic electricity generation from the actual PV system monitoring data to predict electricity generation more accurately in advance. This study is carried out with the following five steps: (i) Data collection of actual electricity generation from PV system and the related information; (ii) Calculation of simulation-based electricity generation; (iii) Comparative analysis between actual electricity generation and simulation-based electricity generation based on the seasonality; (iv) Stochastic approach by defining probability distribution of loss ratio between actual electricity generation and simulation-based electricity generation ; and (v) Case study by conducting Monte-Carlo Simulation (MCS) based on the probability distribution function of loss ratio. The results of this study could be used (i) to estimate electricity generation from PV system more accurately before installation of the system, (ii) to establish the optimal maintenance strategy for the different application fields and the different season, and (iii) to conduct feasibility study on investment at the level of life cycle.

• 설비공학논문집
• /
• 제23권12호
• /
• pp.836-842
• /
• 2011

It has been shown that the air-conditioning system with slab cooling storage is effective in cutting peak load and utilizing nighttime electric power. The stochastic properties of internal heat generation which has great influence on the cooling load are examined in this paper. Based on the measured cooling load and electric power consumption in an office building with slab cooling storage, stochastic time series models to simulate these random processes are investigated. Furthermore, a calculated result by an optimal control method of thermal analysis taking into account the internal heat is compared with the measured cooling load.

• Journal of the Korean Data and Information Science Society
• /
• 제28권6호
• /
• pp.1447-1456
• /
• 2017

강우현상을 구조적으로 모형화한 확률적 강우모의모형의 활용성이 증대되는 상황에서 확률적 강우모의모형의 모수에 대한 정확한 추정은 매우 중요하다. 본 연구에서는 확률적 강우모의모형 (Neyman-Scott rectangular pulse model, NSRPM)의 모수를 DFP (Davidon-Fletcher-Powell), GA (genetic algorithm), Nelder-Mead, DE (differential evolution) 기법으로 추정하고 추정된 모수의 적합성을 분석하고 지역특성에 적합한 모수 추정 기법을 제시하였다. 낙동강 유역의 20개 강우 관측 지점을 대상으로 1973년-2017년 기간 동안의 여름철 1시간 강수자료 이용하여 산정된 모형 모수를 분석한 결과, 전반적으로 DE, Nelder-Mead기법이 가장 좋은 결과를 보였으며 DFP, GA기법은 상대적으로 낮은 적합도를 보였다.

• 설비공학논문집
• /
• 제27권6호
• /
• pp.313-320
• /
• 2015

In this paper, a method to obtain the probability distribution of room temperature and cooling load is presented, when the internal heat generation is applied to the system as a disturbance in the air conditioning system with slab cooling storage. The probability distribution of room temperature and the cooling load due to the disturbance were examined in one room of an office building. When considering only the electric power consumption as a probability component, it was found that the effect on room temperature and cooling load is small, because the probability component of the measured electric power consumption in the building is small. On the other hand, when considering the stochastic fluctuations of electric power consumption together with the heat generated by human bodies, the mean value of the cooling load was about 2,300 W and the ratio of the standard deviations was 19% (10 o'clock in second day). It was revealed that the stochastic effects of internal heat generation acting on the air conditioning system with slab cooling storage are not small.

• Water Engineering Research
• /
• 제4권3호
• /
• pp.111-126
• /
• 2003

Meteorological data are often needed to evaluate the long-term effects of proposed hydrologic changes. The evaluation is frequently undertaken using deterministic mathematical models that require daily weather data as input including precipitation amount, maximum and minimum temperature, relative humidity, solar radiation and wind speed. Stochastic generation of the required weather data offers alternative to the use of observed weather records. The precipitation is modeled by a Markov Chain-exponential model. The other variables are generated by multivariate model with means and standard deviations of the variables conditioned on the wet or dry status of the day as determined by the precipitation model. Ultimately, the objective of this paper is to compare Richardson's model and the improved weather generation model in their ability to provide daily weather data for the crop model to study potential impacts of climate change on the irrigation needs and crop yield. However this paper does not refer to the improved weather generation model and the crop model. The new weather generation model improved will be introduced in the Journal of KWRA.

• 한국시뮬레이션학회:학술대회논문집
• /
• 한국시뮬레이션학회 1999년도 추계학술대회 논문집
• /
• pp.75-80
• /
• 1999

This paper presents a new method to find a optimal solution for stochastic system. This method uses Genetic Algorithm(GA) and simulation. GA is used to search for new alternative and simulation is used to evaluate alternative. The stochastic system has one or more random variables as inputs. Random inputs lead to random outputs. Since the outputs are random, they can be considered only as estimates of the true characteristics of they system. These estimates could greatly differ from the corresponding real characteristics for the system. We need multiple replications to get reliable information on the system. And we have to analyze output data to get a optimal solution. It requires too much computation to be practical. We address the problem of reducing computation. The procedure on this paper use GA character, an iterative process, to reduce the number of replications. The same chromosomes could exit in post and present generation. Computation can be reduced by using the information of the same chromosomes which exist in post and present current generation.

• Water Engineering Research
• /
• 제2권4호
• /
• pp.209-218
• /
• 2001

Generation of evaporation data generally assists in planning, operation, and management of reservoirs and other water works. Annual and monthly evaporation series were generated for King Fahad Dam Lake in Bishah, Saudi Arabia. Data was gathered for period of 22 years. Tests of homogeneity and normality were conducted and results showed that data was homogeneous and normally distributed. For generating annual series, an Autoregressive first order model AR(1) was used and for monthly evaporation series method of fragments was used. Fifty replicates for annual series, and fifty replicates for each month series, each with 22 values length, were generated. Performance of the models was evaluated by comparing the statistical parameters of the generated series with those of the historical data. Annual and monthly models were found to be satisfactory in preserving the statistical parameters of the historical series. About 89% of the tested values of the considered parameters were within the assigned confidence limits

• Journal of Electrical Engineering and Technology
• /
• 제8권1호
• /
• pp.1-10
• /
• 2013

The power industry is currently facing many challenges, due to the new environment created by the introduction of smart grid technologies. In particular, the large-scale deployment of electric vehicles (EVs) may have a significant impact on demand for electricity and, thereby, influence generation and transmission system planning. However, it is difficult to deal with uncertainties in EV charging loads using deterministic planning methods. This paper presents a two-stage stochastic decomposition method with Latin-hyper rectangle sampling (LHRS) to solve the integrated generation and transmission planning problem incorporating EV deployment. The probabilistic distribution of EV charging loads is estimated by Latin-hyper rectangle sampling (LHRS) to enhance the computational performance of the proposed method. Numerical results are presented to show the effectiveness of the proposed method.