• Water Engineering Research
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• v.1 no.4
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• pp.279-291
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• 2000

A mathematical model using dynamic programming approach is applied to an optimal unit commitment problem. In this study, the units are treated as stages instead of as state dimension, and the time dimension corresponds to the state dimension instead of stages. A considerable amount of computer time is saved as compared to the normal approach if there are many units in the basin. A case study on the Lower Colorado River Basin System is presented to demonstrate the capabilities of the optimal scheduling of hydropower units.

• Proceedings of the KIEE Conference
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• 1999.07c
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• pp.1506-1509
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• 1999

This paper presents a new economic dispatch algorithm to improve the unit commitment solution while guaranteeing the near optimal solution without reducing calculation speed. The conventional economic dispatch algorithms have the problem that it is not applicable to the unit commitment formulation due to the frequent on/off state changes of units during the unit commitment calculation. Therefore, piecewise linear iterative method have generally been used for economic dispatch algorithm for unit commitment. In that method, the approximation of the generator cost function makes it hard to obtain the optimal economic dispatch solution. In this case, the solution can be improved by introducing a inverse of the incremental cost function. The proposed method is tested with sample system. The results are compared with the conventional piecewise linear iterative method. It is shown that the proposed algorithm yields more accurate and economical solution without calculation speed reduction.

• Proceedings of the KIEE Conference
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• 1987.11a
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• pp.97-100
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• 1987

An improved hydrothermal unit commitment algorithm is proposed for the purpose of optimal operation of electric power system. Especially, Dynamic Programming Method which is main scheme of the algorithm is modified to assure the feasible solution all the time. The effectiveness of the algorithm has been demonstrated by applying to a sample system.

• Journal of Electrical Engineering and Technology
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• v.9 no.2
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• pp.415-422
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• 2014

This paper presents a new approach to solve the multi area unit commitment problem (MAUCP) using an evolutionary programming based particle swarm optimization (EPPSO) method. The objective of this paper is to determine the optimal or near optimal commitment schedule for generating units located in multiple areas that are interconnected via tie lines. The evolutionary programming based particle swarm optimization method is used to solve multi area unit commitment problem, allocated generation for each area and find the operating cost of generation for each hour. Joint operation of generation resources can result in significant operational cost savings. Power transfer between the areas through the tie lines depends upon the operating cost of generation at each hour and tie line transfer limits. Case study of four areas with different load pattern each containing 7 units (NTPS) and 26 units connected via tie lines have been taken for analysis. Numerical results showed comparing the operating cost using evolutionary programming-based particle swarm optimization method with conventional dynamic programming (DP), evolutionary programming (EP), and particle swarm optimization (PSO) method. Experimental results show that the application of this evolutionary programming based particle swarm optimization method has the potential to solve multi area unit commitment problem with lesser computation time.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.704-706
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• 1996

This paper presents a new algorithm of unit commitment for optimal operation in power system. The proposed method developed algorithm that determined generators considering load variations at each stages. It has established forecast unit commitment over time horizon at first and next calculated quality cost of generators and then committed generator that has minimum quality cost at unit commitment schedule over time horizon. It is used that Objet-Oriented Programming for effective realization, and simple handling of complex program. The proposed method has applied at example system and the results has shown superior economics and computational requirement than the conventional method.

• Proceedings of the KIEE Conference
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• 1991.07a
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• pp.333-336
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• 1991

In this paper, Hopfield & Tank model-like artificial neural network structure is proposed, which can be used for the optimal path planning problems such as the unit commitment problems or the maintenance scheduling problems which have been solved by the dynamic programming method or the branch and bound method. To construct the structure of the neural network, an energy function is defined, of which the global minimum means the optimal path of the problem. To avoid falling into one of the local minima during the optimization process, the simulated annealing method is applied via making the slope of the sigmoid transfer functions steeper gradually while the process progresses. As a result, computer(IBM 386-AT 34MHz) simulations can finish the optimal unit commitment problem with 10 power units and 24 hour periods (1 hour factor) in 5 minites. Furthermore, if the full parallel neural network hardware is contructed, the optimization time will be reduced remarkably.

• Proceedings of the KIEE Conference
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• 1998.07c
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• pp.1140-1142
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• 1998

This paper presents object-oriented dynamic programing formulation of the unit commitment problem. This approach features the classification of generating units into related groups so called class. All object which share the same set of attributes and methods are grouped together in classes and designed inheritance hierarchy to minimize the number of unit combination which must be tested without precluding the optimal path. So this programming techniques will maximize the efficiency of unit commitment.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.89-92
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• 1992

This paper presents an approach for thermal Unit Commitment by Lagrangian Relaxation with fuzzy technique. A proposed algorithm makes it possible to execute optimal decision making between Generation Cost and Load Demand with membership function. In order to test the validity of the proposed method, we applied to Mid-westerm utility system which has 20 thermal units. So, the usefulness of this method is verified.

• Proceedings of the KIEE Conference
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• 2006.07a
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• pp.416-417
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• 2006

The recent movement to deregulated and competitive electricity market requires new concepts against existing central dispatch in the system operation and planning. As power systems tend to be operated more closely to their ultimate ratings, the role of SCOPF(Security Constrained Optimal Power Flow) is changed. This paper deals with the proper Unit Commitment condition changed according to the conditions or configuration of power system. This goal of is paper is to obtain proper security and Optimal UC condition through the efficient usage of the sensitivity Factor against critical contingencies. The proposed mechanism has been tested on a sample system and results show more secure conditions against critical contingencies.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.4
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• pp.457-466
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• 2013

Wind energy is rapidly becoming significant generating technologies in electricity markets. As probabilistic nature of wind energy creates many uncertainties in the short-term scheduling, additional actions for reliable market operation should be taken. This paper presents a novel approach to evaluate ramping capability requirement for changes in imbalance energy between day-ahead market and real-time market due to uncertainty of wind generation as well as system load. Dynamic ramp rate model has been applied for realistic solution in unit commitment problem, which is implemented in day-ahead market. Probabilistic optimal power flow has been used to verify ramping capability determined by the proposed method is reasonable in economic and reliable aspects. This approach was tested on six-bus system and IEEE 118-bus system with a wind farm. The results show that the proposed approach provides ramping capability information to meet both forecasted variability and desired confidence level of anticipated uncertainty.