Optimal unit commitment using equivalent linear minimum up and down time constraints (original) (raw)
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Optimal Unit Commitment Methods of Power System
International Journal of Advance Research and Innovative Ideas in Education, 2017
Unit Commitment helps in making decision that which unit should be running in which period so as to satisfy the varying demand of electricity. In case of electricity the load is higher during the daytime especially in evening when industrial loads are high, lights are on and lower during the night and early morning when most of the population is asleep. Unit commitment helps in deciding which generating unit should be on i.e. to bring the unit up to speed, synchronize it to the system, and connect it so it can deliver power to the network. The paper describes different method used to solve the unit commitment problems. All these methods have some weakness, a comprehensive algorithm that combines the strength of all the methods and overcome each other’s weakness would be a suitable approach for solving unit commitment problems.
Unit Commitment Problem in Electrical Power System: A Literature Review
International Journal of Electrical and Computer Engineering (IJECE), 2018
Unit commitment (UC) is a popular problem in electric power system that aims at minimizing the total cost of power generation in a specific period, by defining an adequate scheduling of the generating units. The UC solution must respect many operational constraints. In the past half century, there was several researches treated the UC problem. Many works have proposed new formulations to the UC problem, others have offered several methodologies and techniques to solve the problem. This paper gives a literature review of UC problem, its mathematical formulation, methods for solving it and Different approaches developed for addressing renewable energy effects and uncertainties.
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In this paper a new approach for solving mixed-integer nonlinear programming problems is presented. The approach is named probabilistic search. The main idea is to search the feasible solution area probabilistically instead of randomly. The presented approach is used to determine the dispatch value of each generator in a pool-based electricity market. In this approach the ON and OFF units are determined using the probabilistic search and the generation value of each unit is determined using the linear programming. A software was developed based on the proposed approach for unit commitment. The software is used in the Iranian electricity power pool now.
International journal of engineering research and technology, 2018
This survey paper helps in the area of scheduling inconvenience of various generating units in electrical power system. It shows several common surveys of developments plus research in area of unit commitment centered on published articles and journals. A detailed survey is done in the sphere of unit commitment for finding different hybrid and non-hybrid methods by means of which unit commitment problem can be solved effectively. It will be quite helpful to the scientists, investigators or researchers employed in the region of the unit
Implementation of Genetic Algorithm for Optimal Unit Commitment in a Power System
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The Genetic Algorithms (GA) are general optimization techniques based on principle inspired from the biological evolution. A simple GA algorithm implementation using the standard crossover and mutation operator could locate near optimal solutions but it most cases failed to converge to optimal solution. However, using the varying quality function techniques and adding problem specific operators, satisfactory solution was obtained. Test result, for systems of up to 100 units and comparisons with result obtained using Lagrangian relaxation and Dynamic programming are also reported. In this research, a genetic algorithm was applied to the unit commitment scheduling problem. A genetic It is hoped that the concurrent processing will enable the algorithm to operate within the needed response time of an electric utility power broker. The goal of this research is to determine if a genetic algorithm can be implemented to find good unit commitment schedules.