Unit commitment using a stochastic extended neighbourhood search Export

@article{citeulike:788777, abstract = {A simulated annealing approach is combined with a tabu search, to develop a robust and powerful optimisation technique for solving the unit commitment problem. The problem is broken down into a combinatorial subproblem in unit status variables and a quadratic programming subproblem in unit power output variables. The combinatorial subproblem is solved using the proposed method. In the hybrid algorithm, which is referred to as a stochastic extended neighbourhood search, simulated annealing is used as the main stochastic algorithm, and a tabu search is used as an extended neighbourhood search, to locally improve the solution obtained by simulated annealing. The neighbourhood search uses local domain-knowledge, which results in rapid convergence of the simulated annealing algorithm. The results obtained for several example systems illustrate the potential of the hybrid approach.}, author = {Purushothama, G. K. and Narendranath, U. A. and Jenkins, L.}, citeulike-article-id = {788777}, citeulike-linkout-0 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1193673}, journal = {Generation, Transmission and Distribution, IEE Proceedings-}, keywords = {energy, new, tabu-search, unit-commitment}, number = {1}, pages = {67--72}, posted-at = {2006-08-07 14:44:43}, priority = {2}, title = {Unit commitment using a stochastic extended neighbourhood search}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1193673}, volume = {150}, year = {2003} }

TY - JOUR ID - citeulike:788777 L3 - citeulike-article-id:788777 N2 - A simulated annealing approach is combined with a tabu search, to develop a robust and powerful optimisation technique for solving the unit commitment problem. The problem is broken down into a combinatorial subproblem in unit status variables and a quadratic programming subproblem in unit power output variables. The combinatorial subproblem is solved using the proposed method. In the hybrid algorithm, which is referred to as a stochastic extended neighbourhood search, simulated annealing is used as the main stochastic algorithm, and a tabu search is used as an extended neighbourhood search, to locally improve the solution obtained by simulated annealing. The neighbourhood search uses local domain-knowledge, which results in rapid convergence of the simulated annealing algorithm. The results obtained for several example systems illustrate the potential of the hybrid approach. IS - 1 JF - Generation, Transmission and Distribution, IEE Proceedings- EP - 72 TI - Unit commitment using a stochastic extended neighbourhood search VL - 150 SP - 67 KW - energy KW - new KW - tabu-search KW - unit-commitment AU - Purushothama, GK AU - Narendranath, UA AU - Jenkins, L PY - 2003/// UR - http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1193673 ER -