• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.397-399
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• 2000

This paper presents a method for assessing reliability indices of transmission system. Because successful operation of electric power under the deregulated electricity market depends on transmission system reliability management, quantity evaluation of transmission system reliability is very important. The key point idea is based on that the reliability level of transmission system is equal to reliability level difference of between composite power system(HLII) and generation system(HLI). It is sure that risk indices of reliability of composite power system are larger than those of generation system. It is the reason that composite power system includes uncertainties and capacity limit of transmission lines. The characteristics and effectiveness of this methodology are illustrated by the case study using MRBTS.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.55 no.10
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• pp.440-445
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• 2006

As an electricity industry transforms into a competitive system, an electricity market revolves into a combined market consisting of generation and operating reserve. This paper presents a market model combined by an energy market and an operating reserve market. In a competitive structure, Gencos strive to choose strategic bidding parameters that maximize total profit resulting from an energy market and a reserve market. The primary goal of the paper is to analyze power transactions of generation and operation reserve based on marginal profits and capacity limits at NE(Nash Equilibrium). In case studies, the reserve market and the energy market are compared at the n from the viewpoints of marginal profits, prices and transaction quantities. It is shown that the marginal profit in an energy market is equal to that in a reserve market, and Gencos strategic bidding is greatly influenced by capacity limit.

• Journal of Electrical Engineering and Technology
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• v.8 no.6
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• pp.1251-1260
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• 2013

This paper presents the optimal scheduling of hourly consumption in a residential community (community, neighborhood, etc.) based on real-time electricity price. The residential community encompasses individual residential loads, communal (shared) loads, and local generation. Community-aggregated loads, which include residential and communal loads, are modeled as fixed, adjustable, shiftable, and storage loads. The objective of the optimal load scheduling problem is to minimize the community-aggregated electricity payment considering the convenience of individual residents and hourly community load characteristics. Limitations are included on the hourly utility load (defined as community-aggregated load minus the local generation) that is imported from the utility grid. Lagrangian relaxation (LR) is applied to decouple the utility constraint and provide tractable subproblems. The decomposed subproblems are formulated as mixed-integer programming (MIP) problems. The proposed model would be used by community master controllers to optimize the utility load schedule and minimize the community-aggregated electricity payment. Illustrative optimal load scheduling examples of a single resident as well as an aggregated community including 200 residents are presented to show the efficiency of the proposed method based on real-time electricity price.

• KIEE International Transactions on Power Engineering
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• v.5A no.4
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• pp.331-338
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• 2005

In this paper, an improved maintenance scheduling approach suitable for the competitive environment is proposed by taking account of profits and costs of generation companies and the formulated combinatorial optimization problem is solved by using Reactive Tabu search (RTS). In competitive power markets, electricity prices are determined by the balance between demand and supply through electric power exchanges or by bilateral contracts. Therefore, in decision makings, it is essential for system operation planners and market participants to take the volatility of electricity price into consideration. In the proposed maintenance scheduling approach, firstly, electricity prices over the targeted period are forecasted based on Artificial Neural Network (ANN) and also a newly proposed aggregated bidding curve. Secondary, the maintenance scheduling is formulated as a combinatorial optimization problem with a novel objective function by which the most profitable maintenance schedule would be attained. As an objective function, Opportunity Loss by Maintenance (OLM) is adopted to maximize the profit of generation companies (GENCOS). Thirdly, the combinatorial optimization maintenance scheduling problem is solved by using Reactive Tabu Search in the light of the objective functions and forecasted electricity prices. Finally, the proposed maintenance scheduling is applied to a practical test power system to verify the advantages and practicability of the proposed method.

• 한국태양에너지학회:학술대회논문집
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• 2008.04a
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• pp.281-286
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• 2008

Current feed-in tariffs(FIT) of Electricity generating from new and renewable energy sources are reappraised with the corrected formula of levelized generation cost(LGC) of utility power. The LGC of new and renewable electricity should be formulated in explicitly reflecting the capital cost and corporate tax during the economic life cycle based on its realistic application data. An applicable term of the FITs should, especially, be equal to the economic life cycle. The revised FITs issued in 2006 were, however, derived from the incorrect formula described in the study of KERI(Korea Electrotechnology Research Institute), and consequently misestimated. The reappraisal values for FIT of new and renewable electricity were shown and interpreted in this paper. An FIT of PV more than 30 kW, for example, should be 972.86 won/kWh instead of current 677.38 won/kWh increasing 43.6%. An upward revision of other FITs for new and renewable electricities should also be required in the range of 8.6% to 47.3%.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.11
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• pp.1503-1510
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• 2014

This study organizes scenarios on the power supply plans and electricity load forecasts considering their uncertainties and estimates natural gas quantity for electricity generation, total electricity supply cost and air pollutant emission of each scenario. Also the analysis is performed to check the properness of government's natural gas demand forecast and the possibility of achieving the government's CO2 emission target with the current plan and other scenarios. In result, no scenario satisfies the government's CO2 emission target and the natural gas demand could be doubled to the government's forecast. As under-forecast of natural gas demand has caused the increased natural gas procurement cost, it is required to consider uncertainties of power plant construction plan and electricity demand forecast in forecasting the natural gas demand. In addition, it is found that CO2 emission target could be achieved by enlarging natural gas use and demand-side management without big increase of total costs.

• Journal of Energy Engineering
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• v.11 no.3
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• pp.210-215
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• 2002

In respect of global environment protection and efficient utilization of energy, co-generation systems, which have greatly higher efficiency than the other generations, have been developed and put to practical use. Assuming that the co-generation system would be operating in Park Hotel, this paper calculates the difference between the heat and electricity rates by the conventional method and the co-generation system, considers the cost of new investment and analyzes introduction economics based on the return on investment. The introduction is turned out to be recommendable, since the return of investment for co-gen oration equipment is about ten years when the co-generation profits in heat and electricity rate. Additionally, accounting for interest rate drop, improvement of environmental matters or electricity rate increase, it is shown to be even more economical.

• Journal of Energy Engineering
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• v.7 no.2
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• pp.223-230
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• 1998

Energy consumption statistics have been surveyed for 50 large commercial buildings with high energy consumption records in Seoul City. The buildings were classified into three different groups for data analysis: hospitals, hotels, and department stores/office buildings. The analysis was focused on identifying installed boiler and refrigerator data, energy consumption rates, and energy load distribution throughout the year. Refrigerating electricity was confirmed again to affect most on the formation of the summer electricity load peak as expected. Replacing the refrigerator electricity peak in the summer with co-generation in large commercial buildings. However, overall heat load distribution in a single building is still considered not large enough for economically feasible co-generation and thus joint co-generation for multiple neighboring buildings are preferred and the Electric Power Law and LNG pricing policy should be revised favorably for co-generation in advance.

• 한국신재생에너지학회:학술대회논문집
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• 2008.05a
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• pp.87-90
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• 2008

Levelized generation cost(LGC) has been widely used in assessing feed-in tariffs(FiT) for electricity generating from new and renewable energies. Current FiTs for renewable electricity in Korea have been fixed and applied with realistic economic data by the efforts of KERI(Korea Electrotechnology Research Institute) since October 2006. Some critical issues on the estimation of LGC are, however, found in KERI's report. Major issues are the estimation of capital cost, the consideration of corporate tax, and the application of economic life cycle in the formulae for LGC. These critical issues are examined and interpreted in a correct way in this paper.

• Journal of the Korean Data and Information Science Society
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• v.28 no.4
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• pp.889-898
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• 2017

In this research multiple change-points estimation for South Korean electricity generation data is considered. We analyze the South Korean electricity data via deterministically trending dynamic time series model with multiple structural changes in trends in a Bayesian approach. The number of change-points and the timing are unknown. The goal is to find the best model with the appropriate number of change-points and the length of the segments. A genetic algorithm is implemented to solve this optimization problem with a variable dimension of parameters. We estimate the structural change-points for South Korean electricity generation data and Nile River flow data additionally.