• Journal of Energy Engineering
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• v.13 no.2
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• pp.128-132
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• 2004

This paper presents a new methodology to get pareto-optimal solution for generation planning. First, we apply dynamic programming, and we can get an optimal economic dispatch considering total quantity of contamination for the specified term. Second, we developed a method which can get pareto-optimal solution. This solution is consisted of a set of optimal generation planning. As a result, decision maker can get pareto-optimal solutions, and can choose a solution. We applied this method to the test system, and showed the usefulness.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.2
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• pp.215-221
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• 2024

The government is encouraging the expansion of renewable energy facilities through national renewable energy policy. However, the installation of renewable energy generation facilities has led to local resident complaints due to landscape degradation, electromagnetic wave emission, real estate devaluation, and environmental pollution. This creates conflicts between power project developers and residents, making the progress of projects more difficult. This study applies non-cooperative game theory to analyze eight cases of renewable energy projects where conflicts between developers and residents were resolved through resident's investment participation. By accepting investments from local stakeholders, residents achieved returns ranging from a maximum of 25 % to a minimum of 4.1 %. It was found through game theory analysis that a dominant strategy involves residents agreeing to the development of the project and the developers sharing a portion of the profits with the residents. The analysis results show that the point where dominant strategy meet forms a Nash equilibrium, and at the same time becomes the Pareto optimal point, benefiting both power generation operators and residents.

• Journal of Korea Water Resources Association
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• v.40 no.9
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• pp.677-685
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• 2007

The objective of this study is to evaluate the applicability of multi-objective genetic algorithm(MOGA) in order to calibrate the parameters of conceptual rainfall-runoff model, Tank model. NSGA-II, one of the most imitating MOGA implementations, is combined with Tank model and four multi-objective functions such as to minimize volume error, root mean square error (RMSE), high flow RMSE, and low flow RMSE are used. When NSGA-II is employed with more than three multi-objective functions, a number of Pareto-optimal solutions usually becomes too large. Therefore, selecting several preferred Pareto-optimal solutions is essential for stakeholder, and preference-ordering approach is used in this study for the sake of getting the best preferred Pareto-optimal solutions. Sensitivity analysis is performed to examine the effect of initial genetic parameters, which are generation number and Population size, to the performance of NSGA-II for searching the proper paramters for Tank model, and the result suggests that the generation number is 900 and the population size is 1000 for this study.

• KDI Journal of Economic Policy
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• v.16 no.2
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• pp.91-109
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• 1994

본고(本稿)는 피보험자(被保險者)의 위험정도(危險程度)에 관하여 정보의 불균형이 존재하고 있는 보험시장에서 전체(全體) 시장참여자(市場參與者)의 효용(效用)과 기대이익(期待利益)을 증대시키기 위하여 어떠한 가격정책을 수립하여야 하는지를 분석(分析)하고, 이 분석을 토대로 현재 정부가 추진중인 가격자유화 추진계획방향을 평가하고 개선방향(改善方向)을 제시하고자 하였다. 본고(本稿)의 분석결과(分析結果)에 따르면 정보(情報)의 불균형하(不均衡下)에서 자유화 초기단계의 제한적인 가격자유화는 전체 보험이용자의 효용을 증대시키는 효과를 가져오나 자유화의 폭이 커질수록 사회전체적인 효용증대효과는 불명확해진다. 이 경우 일정범위에 대해서는 요율(料率)과 보장범위(保障範圍)를 위험에 따라 차별화하지 않는 단일(單一) 공동요율(共同料率)을 제시하는 계약(契約)을 주계약(主契約)으로 구매하게 하고, 보충계약(補充契約)인 특약부분(特約部分)에서 각 이용자가 위험정도에 따라 차별화된 가격으로 적절한 보장범위를 선택하게 하도록 정부가 유도하는 것이 바람직하다. 주계약과 보충계약으로 구성된 이러한 보조계약(補助契約)은 기존의 단일(單一) 공동요율(共同料率)에 의한 계약보다 파레토개념에서 우월한 계약으로 시장전체에 순효용증대(純效用增大)의 효과(效果)를 가져올 것이다. 또한 고지의무(告知義務)의 강화(强化), 위험분류(危險分類) 및 선택(選擇) 업무(業務)의 효율화(效率化) 등으로 보험시장내에서 정보의 불균형현상이 해소되고 나면 실질적(實質的)인 가격(價格)의 완전자유화(完全自由化)를 실시하여 파레토최적(最適)을 이루어야 할 것이다. 따라서 정부는 보험시장(保險市場)의 특성(特性)을 고려하여 주어진 조건에서 전체 시장참여자의 효용과 기대이익을 극대화하는 가격정책(價格政策)을 펴야 하며, 현재 추진중인 보험상품(保險商品) 가격자유화계획(價格自由化計劃)도 이런 관점에서 재조명되어야 할 것이다.

• Journal of Korea Water Resources Association
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• v.40 no.9
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• pp.687-696
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• 2007

Preference ordering approach is applied to optimize the parameters of Tank model using multi-objective genetic algorithm (MOGA). As more than three multi-objective functions are used in MOGA, too many non-dominated optimal solutions would be obtained thus the stakeholder hardly find the best optimal solution. In order to overcome this shortcomings of MOGA, preference ordering method is employed. The number of multi-objective functions in this study is 4 and a single Pareto-optimal solution, which is 2nd order efficiency and 3 degrees preference ordering, is chosen as the most preferred optimal solution. The comparison results among those from Powell method and SGA (simple genetic algorithm), which are single-objective function optimization, and NSGA-II, multi-objective optimization, show that the result from NSGA-II could be reasonalby accepted since the performance of NSGA-II is not deteriorated even though it is applied to the verification period which is totally different from the calibration period for parameter estimation.