Security-constrained unit commitment planning using particle swarm optimization

This investigation explores the development of day-ahead security-constrained unit commitment (SCUC) plans for electrical power systems. Such plans involve the coordination of power system generators in response to variations in loading conditions over a twenty-four hour period. This plan must minimize costs associated with fuel consumption and system losses while satisfying all operational constraints.;This investigation proposes a novel hybrid approach involving the use of particle swarm optimization (PSO) for SCUC planning. As this biologically-inspired methodology is both robust and is based on an advanced strategy for exploring large search-spaces, it is well suited for this highly-constrained power systems problem.;A proposed methodology is implemented in software and a series of test cases are used to assess its functionality. The results of the simulations indicate that the software produces generation schemes that meet all system constraints and that have lower operating costs than those produced with linear programming methodologies. The hybrid PSO solution may therefore be seen as an effective tool for SCUC planning.