Authors : T. Sree Renga Raja , N.S. Marimuthu and T. Sree Sharmila
Abstract: Reliable power production is critical to the profitability of electricity utilities. Power generators need to be scheduled efficiently to meet electricity demand. This dissertation develops a solution method to schedule units for producing electricity while determining the estimated amount of surplus power each unit should produce taking into consideration the stochasticity of the load and its correlation structure. This scheduling problem is known as the dispatch problem in the power industry. A general formulation and the development of cascade correlation algorithm to solve the environmentally constrained dispatch problem are presented. The objective is the minimization of the cost of operation, subject to all the usual and emissions constraints. The algorithm handles multiple pollutants and for each pollutant the constraints include the maximum hourly emission on every unit, the maximum hourly emission on every set of on-line units and the maximum daily emission for the system constraints. Three closed-form dispatch strategies and two feasibility conditions are established to eliminate unfeasible unit combinations thus rendering a very efficient commitment algorithm. Test results are provided to show the efficiency of the proposed method.
T. Sree Renga Raja , N.S. Marimuthu and T. Sree Sharmila , 2006. Neural Based Generation Scheduling with Environmental Constraints. International Journal of Soft Computing, 1: 91-96.