Towards more cost saving under stricter ramping rate constraints of dynamic economic dispatch problems - a genetic based approach

Abstract

Due to the dynamic ramping rate constraint, the dynamic economic dispatch (DED) problem generates extra burden for the conventional deterministic techniques. These techniques run the high risk of false dispatches, where a cost optimal solution may violate the power limits and/or ramping rate constraint, and power redispatch becomes essential to force the solution to be inside the feasible region. The operational strategy thus obtained may not be optimal, and the search procedure can be time consuming. By working at the reverse way, genetic algorithms only generate and search for the optimal solutions within the feasible operating region, and gradually move them towards the cost optimal solutions. In this way, not only the ramping rate constraint imposes no extra burden to the GA search, it in turn helps in finding a better strategy to operate power system smoother, safer and more economic. The suitability and capability of the GAs in dealing with the rate constraint on the DED problem is clearly demonstrated in this paper on a 25 generator system which is derived from a practical supply system-Northern Ireland Electricity (NIE).