• Proceedings of the Korea Society for Energy Engineering kosee Conference
• /
• 2000.11a
• /
• pp.221-227
• /
• 2000

The optimal capacity of CGS(Co-Generation Sys.) for APT. complex is 300kW output of electricity power from being considered the simple payback period, loss and benefit, and the running mode of CGS. The proper operation mode of CGS is as follows; If the demanding electricity load of APT. complex is within the range of capacity of generator(300kW), CGS is connected and operated with KEPCO grid. When the load ratio is over 50% of normal load of CGS(300kW), only CGS supply electricity demand. If not, the electricity line of CGS is exchanged to that of KEPCO.

• Korean Management Science Review
• /
• v.26 no.1
• /
• pp.77-91
• /
• 2009

It is the most important sucess factor for the electricity generation industry to minimize operations cost of surplus electricity generation through accurate demand forecasts. Temperature forecast is a significant input variable, because power demand is mainly linked to the air temperature. This study estimates the information value of the temperature forecast by analyzing the relationship between electricity load and daily air temperature in Korea. Firstly, several characteristics was analyzed by using a population-weighted temperature index, which was transformed from the daily data of the maximum, minimum and mean temperature for the year of 2005 to 2007. A neural network-based load forecaster was derived on the basis of the temperature index. The neural network then was used to evaluate the performance of load forecasts for various types of temperature forecasts (i.e., persistence forecast and perfect forecast) as well as the actual forecast provided by KMA(Korea Meteorological Administration). Finally, the result of the sensitivity analysis indicates that a $0.1^{\circ}C$ improvement in forecast accuracy is worth about $11 million per year.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.62 no.1
• /
• pp.21-28
• /
• 2013

The paper proposes an optimal sizing method of a customer's battery energy storage system (BESS) which aims at managing the electricity demand of the customer to minimize electricity cost under the time of use(TOU) pricing. Peak load limit of the customer and charging and discharging schedules of the BESS are optimized on annual basis to minimize annual electricity cost, which consists of peak load related basic cost and actual usage cost. The optimal scheduling is used to assess the maximum cost savings for all sets of candidate capacities of BESS. An optimal size of BESS is determined from the cost saving curves via capacity of BESS. Case study uses real data from an apartment-type factory customer and shows how the proposed method can be employed to optimally design the size of BESS for customer demand management.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.10
• /
• pp.2701-2717
• /
• 2023

In this paper, a hybrid fuzzy-based method is suggested for determining India's best system for power generation. This suggested approach was created using a fuzzy-based combination of the Giza Pyramids Construction (GPC) and Recalling-Enhanced Recurrent Neural Network (RERNN). GPC is a meta-heuristic algorithm that deals with solutions for many groups of problems, whereas RERNN has selective memory properties. The evaluation of the current load requirements and production profile information system is the main objective of the suggested method. The Central Electricity Authority database, the Indian National Load Dispatch Centre, regional load dispatching centers, and annual reports of India were some of the sources used to compile the data regarding profiles of electricity loads, capacity factors, power plant generation, and transmission limits. The RERNN approach makes advantage of the ability to analyze the ideal power generation from energy data, however the optimization of RERNN factor necessitates the employment of a GPC technique. The proposed method was tested using MATLAB, and the findings indicate that it is effective in terms of accuracy, feasibility, and computing efficiency. The suggested hybrid system outperformed conventional models, achieving the top result of 93% accuracy with a shorter computation time of 6814 seconds.

• Proceedings of the KIEE Conference
• /
• 2002.07a
• /
• pp.395-397
• /
• 2002

Recently, electric heating using midnight power has brought a rapid increase. Therefore, the increase of midnight power use has much influenced on midnight load pattern and share more than 20% of total midnight electricity consumption. Hence, the distortion of load by using midnight power apparatus is severe and made the usage pattern ineffective electricity usage pattern. This paper analyze the load pattern of midnight power apparatus and policies that can effectively build midnight load. In addition, it provides policies on shifting midnight power apparatus load.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.64 no.9
• /
• pp.1276-1280
• /
• 2015

Among the various statistical factors for South Korea, the population has been steadily decreased by lower birthrate. Nevertheless, the number of household is constantly increasing amid population aging and single life style. In general, residential electricity use is more the result of the number of household than the population. Therefore, residential electricity consumption is expected to be far higher for decades to come. The existing long-term load forecasting, however, do not necessarily reflect the growth of single and two-member households. In this respect, this paper proposes the long-term load forecasting for residential users considering the effect of changes of the housing type, and in the case study the changes of the residential load pattern is analyzed for accurate long-term load forecasting.

• Journal of the Korean Solar Energy Society
• /
• v.29 no.4
• /
• pp.48-57
• /
• 2009

Energy demands for hotels in Korea are surveyed and statistically analyzed to develop calculation models for a simulation. Daily energy loads of 16 hotels located in Seoul, Busan, Daegu, Inchon, and Daejon are analyzed based on energy log sheets. Detailed hourly loads are field measured for 3 hotels that are carefully selected among the surveyed. One of the salient features for energy consumption by hotels is their weekly periodicity. Relatively large values of deviations are observed for both heat and electricity loads through the country. The mains factors are: location, hotel grade (luxuriousness) and insulation. Detailed quantitative information such are annual average, daily variation, and hourly patterns are provided.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
• /
• v.30 no.3
• /
• pp.130-142
• /
• 2018

We use heat pumps with thermal storage system to reduce peak usage of electric power during winters and summers. A heat pump stores thermal energy in a thermal storage tank during the night, to meet load requirements during the day. This system stabilizes the supply and demand of electric power; moreover by utilizing the inexpensive midnight electric power, thus making it cost effective. In this study, we propose a system wherein the thermal storage tank and heat pump are modeled using the TRNSYS, whereas the control simulations are performed by (i) conventional control methods (i.e., thermal storage priority method and heat pump priority method); (ii) region control method, which operates at the optimal part load ratio of the heat pump; (iii) load response control method, which minimizes operating cost responding to load; and (iv) dynamic programming method, which runs the system by following the minimum cost path. We observed that the electricity cost using the region control method, load response control approach, and dynamic programing method was lower compared to using conventional control techniques. According to the annual simulation results, the electricity cost utilizing the load response control method is 43% and 4.4% lower than those obtained by the conventional techniques. We can note that the result related to the power cost was similar to that obtained by the dynamic programming method based on the load prediction. We can, therefore, conclude that the load response control method turned out to be more advantageous when compared to the conventional techniques regarding power consumption and electricity costs.

• Journal of Electrical Engineering and Technology
• /
• v.13 no.3
• /
• pp.1069-1078
• /
• 2018

Demand response (DR) programs give opportunity to consumers to manage their electricity bills. Besides, distribution system operator (DSO) is interested in using DR programs to obtain technical and economic benefits for distribution network. Since small consumers have difficulties to individually take part in the electricity market, an entity named demand response provider (DRP) has been recently defined to aggregate the DR of small consumers. However, implementing DR programs face challenges to fairly allocate benefits and payments between DRP and DSO. This paper presents a procedure for modeling the interaction between DRP and DSO based on a bilevel programming model. Both DSO and DRP behave from their own viewpoint with different objective functions. On the one hand, DRP bids the potential of DR programs, which are load shifting and load curtailment, to maximize its expected profit and on the other hand, DSO purchases electric power from either the electricity market or DRP to supply its consumers by minimizing its overall cost. In the proposed bilevel programming approach, the upper level problem represents the DRP decisions, while the lower level problem represents the DSO behavior. The obtained bilevel programming problem (BPP) is converted into a single level optimizing problem using its Karush-Kuhn-Tucker (KKT) optimality conditions. Furthermore, point estimate method (PEM) is employed to model the uncertainties of the power demands and the electricity market prices. The efficiency of the presented model is verified through the case studies and analysis of the obtained results.

• Proceedings of the KIEE Conference
• /
• 2000.07d
• /
• pp.2825-2827
• /
• 2000

In electric power industry. load balance has been one of the most fundamental and important management goals. Therefore. the strategy to achieve high quality load management now includes load balance besides the stabilization of electricity supply and quality management of electricity. Amongst many techniques of load management. direct load management has been actively studied and utilized to increase power facility and peak load suppression. Higher peak load situation is appeared during summer than during winter in Korea. and approximately 20% of the peak load is due to the load for air-conditioning. To cope with this peak load problem during summer KEPCO is performing a research project to develop a system to remotely control air-conditioning load using wireless communication. Currently, applicable facilities are limited to small-scale air-conditioning facility that has less than 2KW power capacity. This paper described the 1st year of efforts made in the study.