A solution to the unit-commitment problem using integer-coded genetic algorithm

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Optimal Allocation and Economic Analysis of Energy Storage System in Microgrids (2011, background): “During the last decade, GA has been successfully applied to diverse power systems problems: optimal design of control systems [25]; unit commitment [26]; generation scheduling and its sub problems [ 27 ].”
Differential Evolution Algorithm for Solving Unit Commitment with Ramp Constraints (2008, methodology): “The integer code proposed in [21] is used, where an integer value with a negative sign indicates the number of hours for which a unit is in ‘off’ state.”
A new MILP based approach for unit commitment in power production planning (2013, neutral): “Instance Size ICGA [29] GRASP [13] CON [30] P1 10 566 404 565 825 565 825 P2 20 1 127 244 1 126 805 1 126 070 P3 40 2 254 123 2 255 416 2 248 490 P4 60 3 378 108 3 383 184 3 370 530 P5 80 4 498 943 4 524 207 4 494 140 P6 100 5 630 838 5 668 870 5 615 410”
Stochastic Multiobjective Market Clearing of Joint Energy and Reserves Auctions Ensuring Power System Security (2009, methodology): “On the basis of different load forecast levels and their probabilities obtained form the probability distribution function, roulette wheel mechanism [22], [23] is implemented to generate scenarios for each hour.”