A genetic algorithm for solving the unit commitment problem of a hydro-thermal power system

The paper presents a two layer approach to solve the unit commitment problem of a hydro-thermal power system. The first layer uses a genetic algorithm (GA) to decide the on/off status of the units. The second layer uses a nonlinear programming formulation solved by a Lagrangian relaxation to perform the economic dispatch while meeting all plant and system constraints. In order to deal effectively with the constraints of the problem and prune the search space of the GA in advance, the difficult minimum up/down-time constraints of thermal generation units and the turbine/pump operating constraint of storage power stations are embedded in the binary strings that are coded to represent the on/off-states of the generating units. The other constraints are handled by integrating penalty costs into the fitness function. In order to save execution time, the economic dispatch is only performed if the given unit commitment schedule is able to meet the load balance, energy, and begin/end level constraints. The proposed solution approach was tested on a real scaled hydro-thermal power system over a period of a day in half-hour time-steps for different GA-parameters. The simulation results reveal that the features of easy implementation, convergence within an acceptable execution time, and a highly optimal solution in solving the unit commitment problem can be achieved