• 한국태양에너지학회 논문집
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• 제37권1호
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• pp.59-69
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• 2017

In 2012, Korea introduced a Renewable Portfolio Standard (RPS) scheme, replacing the Feed-in Tariff (FIT) scheme as a market support policy of renewable energy in the electricity market. RPS is to allocate obligatory quota of renewable energy sources for electricity suppliers, whereas FIT is to guarantee high prices for electricity from renewable energy sources. This study examines the effect of this policy change on solar photovoltaic market. According to the study, solar PV market grew fast under FIT as well as under RPS. However, under RPS the size of subsidy for solar PV suppliers was shrunk substantially. In addition, market risk increased severly under RPS due to the volatility of price of renewable energy certificate (REC) as well as of the electricity market price. The small and medium suppliers of solar PV were suffered the most severly from these policy effects. Therefore, the policy reform of RPS is needed to alleviate the market risk of small and medium suppliers of solar PV.

• 에너지공학
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• 제24권2호
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• pp.59-71
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• 2015

본 논문에서는 산업연관분석을 이용하여 한국과 일본 전력산업의 국민경제적 파급효과를 분석하고자 한다. 먼저 수요유도형 모형을 이용하여 생산유발효과, 부가가치 유발효과를 살펴본다. 아울러 공급유도형 모형 및 레온티에프 가격모형을 적용하여 전력산업의 공급지장효과와 물가파급효과에 대하여 한국과 일본을 비교 분석한다. 이러한 분석은 전력산업을 외생화하여 이루어지게 된다. 분석결과 전력산업에서의 1원(엔) 생산이 타 산업에 유발하는 생산유발효과는 한국 0.5946원, 일본 0.5446엔 이었으며, 타 산업에 유발하는 부가가치 유발효과는 한국 0.1716원, 일본 0.2929엔이었다. 전력산업의 공급지장효과는 한국 1.5932원, 일본 1.2801엔이었으며, 전력산업의 10% 가격 인상으로 인한 물가파급효과는 한국 0.2113%, 일본 0.2196%로 한국이 높게 나타났다.

• Journal of Electrical Engineering and Technology
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• 제3권1호
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• pp.14-19
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• 2008

The paper presents an intelligent time series model to predict uncertain electricity market price in the deregulated industry environment. Since the price of electricity in a deregulated market is very volatile, it is difficult to estimate an accurate market price using historically observed data. The parameter of an intelligent time series model is obtained based on the simultaneous perturbation stochastic approximation (SPSA). The SPSA is flexible to use in high dimensional systems. Since prediction models have their modeling error, an error compensator is developed as compensation. The SPSA based intelligent model is applied to predict the electricity market price in the Pennsylvania-New Jersey-Maryland (PJM) electricity market.

• Journal of Electrical Engineering and Technology
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• 제5권1호
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• pp.1-7
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• 2010

Recently, smart grid has been considered a very important new energy delivery technology, and one that can help ensure a cleaner environment by making use of information and communication technology (ICT) in countries around the world. The many technological benefits smart grid offers is expected to bring about a huge change in the electric energy supply chain. In particular, smart grid with advanced ICT is likely to allow market agents to participate in the decision-making process in the restructured electricity industry, easily facilitating Homeostatic Utility Control. In this paper, we examine smart grid as a market externality, and then illustrate issues from the commercial market perspective as it relates to electricity market design. Finally, our paper identifies some of the impacts of smart grid on electricity market design, which may possibly be incorporated into the evolution of the electricity market, thus ensuring market efficiency.

• Journal of Information Processing Systems
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• 제11권1호
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• pp.116-124
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• 2015

Smart grids propose new solutions for electricity consumers as a means to help them use energy in an efficient way. In this paper, we consider the demand-side management issue that exists for a group of consumers (houses) that are equipped with renewable energy (wind turbines) and storage units (battery), and we try to find the optimal scheduling for their home appliances, in order to reduce their electricity bills. Our simulation results prove the effectiveness of our approach, as they show a significant reduction in electricity costs when using renewable energy and battery storage.

• International Journal of Computer Science & Network Security
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• 제24권6호
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• pp.77-82
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• 2024

The Smart home energy consumption represents much of the total energy consumed in advanced countries. For this reason, the main objectif of this paper is to study the energy consumption profile by day for each home appliances: controllable appliances for example Washing machine, Tumble dryer and Air conditioning and uncontrollable appliances for example TV, PC, Lighting, Refrigerator and Electric heater. In this paper, we start with presentation of a smart home energy management systems. Next, we present the modeling and simulation of controllable appliances and uncontrollable appliances. Finally, concludes this paper with some prospects. The modeling and the simulation of a Smart home appliances is based on MATLAB/Simulink software.

• 전기학회논문지
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• 제63권12호
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• pp.1627-1636
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• 2014

In real-time electricity price environment, the energy management system can provide the significant advantage to the residential, commercial and industrial customers since it can reduce the electricity charge by controlling the load operation effectively in response to time-varying price. However, the earlier studies for load management mainly focus on the residential and commercial customers except for the industrial customers because most of load operations in industrial sector are intimately related with production schedule. So, it is possible that the inappropriate control of loads in industrial sector causes huge economic loss. In this paper, therefore, we propose load control algorithm for factory energy management system(F-EMS) to achieve not only minimizing the electricity charges but also maintaining production efficiency by considering characteristics of load operation and production schedule. Considering characteristics of load operation and production schedule, the proposed load control algorithm can reflect the various characteristics of specific industrial customer and control their loads within the range that the production efficiency is maintained. Simulation results show that the proposed load control algorithm for F-EMS leads to significant reduction in the electricity charges and peak power in industrial sector.

• 설비공학논문집
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• 제22권1호
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• pp.49-55
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• 2010

The objectives of this study are to analyze the energy independence plan and to propose a suitable sewage treatment plant in Korea. The total amount of electricity consumption for public sewage treatment plant was estimated as 1,812 GWh in 2007. It was estimated that total 16 sewage treatment plants with renewable energy systems produced electricity of 15 GWh per year, which could replace 0.8% of total electricity used for sewage treatment. It was found that domestic sewage treatment plants with power generation plants by digestion gas were installed in 7 places and produced electricity of 13 GWh per year. It was also found that the power generation plants by digestion gas were the most cost-effective for sewage treatment plant out of the renewable energy systems based on the benefit-cost analysis.

• 전기학회논문지
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• 제64권2호
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• pp.222-227
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• 2015

This paper presents the optimal energy generation systems for economical EVs(Electric Vehicles) charging stations located in an island area. The system includes grid electricity, diesel generator and renewable energy sources of wind turbines and PV(Photovoltaic) panels. The independent generation system is designed with data resources such as annual average wind speed, solar radiation and the grid electricity price by calculating system cost under different structures. This sensitive analysis on the varying data resources allows for the configuration of the most economical generation system for charging stations by comparing initial capital, operating cost, NPC(Net Present Cost) and COE(Cost of Energy). Depending on the increase of the grid cost, the NPC variation of the most economical system which includes renewable energy generations and grid electricity can be smaller than those of other generation systems.

• 경영과학
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• 제30권1호
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• pp.139-148
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• 2013

A nuclear energy has been one of the most important sources to securely supply electricity in South Korea. Its weight in the national electricity supply has kept increasing since the first nuclear reactor was built in 1978. The country relies on the nuclear approximately 31.4% in 2012 and it is expected to increase to 48.5% in 2024 based on the long-term electricity supply plan announced by the Korean government. However, Fukushima disaster due to 9.0 magnitude earthquake followed by the tsunami has raised deep concerns on the security of the nuclear power plants. The policy makers of the country are much interested in analyzing the cost structure of the power supply in the case that the nuclear is diminished from the current supply portion. This research uses a stochastic model that aims to evaluate the long-term power supply plan and provides an extensive cost analysis on the changes of the nuclear power supply. To evaluate a power supply plan, the research develops a few plausible energy mix scenarios by changing the installed capacities of energy sources from the long-term electricity supply plan. The analyses show that the nuclear is still the most attractive energy source since its fuel cost is very much stable compared to the other sources. Also the results demonstrate that a large amount of financial expenditure is additionally required every year if Koreans agree on the reduction of nuclear to increase national security against a nuclear disaster.