• The Transactions of the Korean Institute of Electrical Engineers A
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• v.49 no.2
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• pp.70-77
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• 2000

This paper proposes a method of solving a unit commitment problem using tabu search (TS) which is heuristic algorithm. Ts is a local search method that starts from any initial solution and attempts to determine a better solution using memory structures. In this paper, to reduce the computation time for finding the optimal solution, changing tabu list size as intensification strategy and path relinking method as diversification strategy are proposed. To show the usefulness of the proposed method, we simulated for 10 units system and 110 units system. Numerical results show improvements in the generation costs and the computation time compared with priority list, genetic algorithm(GA), and hybrid GA.

• Journal of international Conference on Electrical Machines and Systems
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• v.2 no.3
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• pp.367-374
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• 2013

The electrical vehicles (EV) with vehicle-to-grid (V2G) capability can be used as loads, energy sources and energy storage in MicroGrid integrated with renewable energy sources. The output power of generators will be reallocated in the considering of V2G. An intelligent unit commitment (UC) with V2G for cost optimization is presented in this paper. A new constraint of UC with V2G is considered to satisfy daily use of EVs. A hybrid optimiza-tion algorithm combined Binary Particle Swarm Optimization (BPSO) with Lagrange Mul-tipliers Method (LMM) is proposed. The difference between results of UC with V2G and UC without V2G is presented.

• KIEE International Transactions on Power Engineering
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• v.5A no.1
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• pp.16-21
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• 2005

In this study, a new Unit Commitment (UC) algorithm is proposed to consider the uncertainty of a daily load profile. The proposed algorithm calculates the UC results with a lower load level than that generated by the conventional load forecast method and the greater hourly reserve allocation. In case of the worst load forecast, the deviation of the conventional UC solution can be overcome with the proposed method. The proposed method is tested with sample systems, which indicates that the new UC algorithm yields a completely feasible solution even when the worst load forecast is applied. Also, the effects of the uncertain hourly load demand are statistically analyzed, particularly by the consideration of the average over generation and the average under generation. Finally, it is shown that independent power producers participating in electricity spot-markets can establish bidding strategies by means of the statistical analysis. Therefore, it is expected that the proposed method can be used as the basic guideline for establishing bidding strategies under the deregulation power pool.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.43 no.5
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• pp.739-746
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• 1994

This paper present an efficient methodology to solve the unit commitment problem for large scaled power system which involves various type of generation. We introduce the global optimization approach to coordinate the thermal type, hydro type and pumped storage type generation. To overcome the shortcomings in dynamic programming for thermal unit commitment, an improved heurisitic method using lambda(λ) was proposed, Hydro and pumped type allocation was Solved by analytical approach using λ which exculde undisirable iteration for satisfying the energy usage constraints. The case studies for proposed algorithm are proven by sample system and KEPCO practical system, which produced very resonable both computing requirements and convergency.

• Proceedings of the KIEE Conference
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• 1996.11a
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• pp.137-140
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• 1996

This paper proposes a unit commitment scheduling method based on Parallel Genetic Algorithm(PGA). Due to a variety of constraints to be satisfied, such as the minimum up and down time constraints, the search space of the UC problem is highly nonconvex. So, we used transputer which is one of the practical parallel processors. It can give us fastness and effectiveness features of the proposed method for solving the problem. To show the effectiveness of the PGA based unit commitment scheduling, we tested results for system of 5 units and we can get desirable results.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.6
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• pp.708-714
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• 2018

Islanded operation of a microgrid can ensure the reliable operation of the system when a large accident occurs in the main grid. However, because the generation capability of a microgrid is typically limited, a microgrid operator should take islanded operation risk into account in scheduling its generation resources. To address this problem, in this paper we have proposed two unit commitment formulations based on the islanding scenario that reflect the expected and worst-case values of the islanded operation risk. An optimal resource scheduling strategy is obtained for the microgrid operator by solving these optimization problem, and the effectiveness of the proposed method is investigated by numerical simulations.

• Journal of information and communication convergence engineering
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• v.1 no.2
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• pp.93-97
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• 2003

This paper proposes a new generation scheduling algorithm which can consider total quantity of contamination and energy limits based on Lagrangian Relaxation. First, we formulate a Lagrangian function which consider quantity of contamination including substitution of fuel, and quantity of fuel. Second, we developed Multi-state S.U.D.P. which can treat multiple fuels. Third, we propose an effective generation scheduling algorithm including Multi-state S.U.D.P.. As a results, unit commitment and generating power can be obtained to meet the environmental and fuel constraints with fuel substitution. By applying it to the test system, effectiveness of the method is verified.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.55 no.9
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• pp.365-375
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• 2006

This paper is presented by the application of parallel adaptive evolutionary algorithm(PAEA) to search an optimal solution of a thermal unit commitment problem. The adaptive evolutionary algorithm(AEA) takes the merits of both a genetic algorithm(GA) and an evolution strategy(ES) in an adaptive manner to use the global search capability of GA and the local search capability of ES. To reduce the execution time of AEA, the developed algorithm is implemented on an parallel computer which is composed of 16 processors. To handle the constraints efficiently and to apply to Parallel adaptive evolutionary algorithm(PAEA), the states of thermal unit are represented by means of real-valued strings that display continuous terms of on/off state of generating units and are involved in their minimum up and down time constraints. And the violation of other constraints are handled by repairing operator. The procedure is applied to the $10{\sim}100$ thermal unit systems, and the results show capabilities of the PAEA.

• Journal of Korean Academy of Nursing Administration
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• v.8 no.4
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• pp.551-561
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• 2002

Purpose : The purpose of the present study was to examine the causal relationships among head nurses' leadership (consideration oriented leadership, structure initiated leadership), nursing unit culture (human development culture, task development culture), and nurses' organizational commitment. Method : Four large general hospitals located in and around Seoul were selected to participate. The total sample of 286 registered nurses represents a response rate of 90 percent. Data for this study was collected from August to September in 2000 by questionnaire. Path analyses with LISREL program were used to test the fit of the proposed model to the data and to examine the causal relationships among variables. Result : Both the proposed model and the modified model fit the data adequately. The model revealed relatively high explanatory power. 52% of human development culture was explained by predicted variables and 44% of task development was explained by predicted variables; 47% of the organizational commitment was explained by predicted variables. In predicting developmental (both human and task) nursing unit culture, the findings of this study clearly demonstrated that head nurses' consideration oriented leadership might be the most important variable. The result also showed that head nurses' consideration oriented leadership might be the most important variable among the predicted variables in predicting organizational commitment. Conclusion : Based on the findings of this study, it was suggested that various studies, for example, matching leadership and nursing unit culture, were needed.

• Journal of Korean Academy of Nursing Administration
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• v.21 no.2
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• pp.203-211
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• 2015

Purpose: This study aimed to investigate job satisfaction, organizational commitment and turnover intention among male nurses in South Korea. Methods: Data were collected in May, 2013 and a total of 150 male hospital nurses participated in this study. Job satisfaction, organizational commitment and turnover intention were measured using a structured questionnaire. Collected data were analyzed using SPSS/WIN 20.0 for windows. Results: There were statistically significant correlations between job satisfaction and organizational commitment (r=.74, p<.001), job satisfaction and turnover intention (r=-.56, p<.001), and organizational commitment and turnover intention (r=-.69, p< .001). There was a statistically significant difference in job satisfaction of participants according to education, work unit, and salary. Organizational commitment showed significant difference according to age, education, work unit, and salary. Turnover intention was significantly different according to duration of employment in the hospital, and salary. The most influential factor for turnover intention was organizational commitment (${\beta}=-.69$, p<.001). This factor accounted for 47.8% of the variance in turnover intention. Conclusion: The results of this study indicate that to reduce turnover intention for men in nursing, it is necessary to increase job satisfaction, and organizational commitment.