Unit commitment in a power generation system using a modified improved-dynamic programming

In this paper, various techniques or methods are considered to develop an optimal unit commitment schedule that significantly minimizes cost at a reduced computation time, while meeting power gaining time cannot be overemphasized, as an increased time could translate demand. Several robust methods have obtained good solutions but with a penalty of high computational time. The importance of to a rise in cost. The UC formulation is characterized by an objective function that is optimized with respect to certain well defined power generation constraints. They include: hourly power demand / load, ramp up / down limits, minimum up / down times, maximum and minimum power generated or loading limits, etc. The focus of this paper is the application of the proposed, modified improved-dynamic programming method to optimize the UC formulation, to achieve the desired objective of trimming down the convergence time of the UC problem and in the process improve or maintain the overall cost solution quality. Three test case data are used from already existing papers to compare with the new solution obtained. The convergence time for case 1, case 2, and case 3 is reduced significantly by some seconds and is expressed in percentage of 1.14%, 4.01% and 8.60% respectively.

[1]  S. M. Shahidehpour,et al.  Hydro-thermal, scheduling by tabu search and decomposition method , 1996 .

[2]  W. J. Hobbs,et al.  An enhanced dynamic programming approach for unit commitment , 1988 .

[3]  Prateek Kumar Singhal,et al.  Dynamic programming approach for solving power generating unit commitment problem , 2011, 2011 2nd International Conference on Computer and Communication Technology (ICCCT-2011).

[4]  I. Erlich,et al.  A new approach for solving the unit commitment problem by adaptive particle swarm optimization , 2008, 2008 IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century.

[5]  Chuan-Ping Cheng,et al.  Unit commitment by Lagrangian relaxation and genetic algorithms , 2000 .

[6]  Arthur I. Cohen,et al.  A Method for Solving the Fuel Constrained Unit Commitment Problem , 1987, IEEE Transactions on Power Systems.

[7]  Arthur I. Cohen,et al.  A Branch-and-Bound Algorithm for Unit Commitment , 1983, IEEE Transactions on Power Apparatus and Systems.

[8]  R. Janani,et al.  Optimization of Unit Commitment Problem and Constrained Emission using Genetic Algorithm , 2013 .

[9]  Farid Benhamida,et al.  Thermal unit commitment solution using an improved Lagrangian Relaxation , 2007 .

[10]  V. S. Senthil Kumar,et al.  Solution to security constrained unit commitment problem using genetic algorithm , 2010 .

[11]  Ross Baldick,et al.  The generalized unit commitment problem , 1995 .

[12]  Roy Billinton,et al.  Unit commitment in interconnected generating systems using a probabilistic technique , 1990 .

[13]  A. Y. Abdelaziz,et al.  An Augmented Hopfield Neural Network for Optimal Thermal Unit Commitment , 2010 .

[14]  M. Carrion,et al.  A computationally efficient mixed-integer linear formulation for the thermal unit commitment problem , 2006, IEEE Transactions on Power Systems.