Optimization of Large-Scale Dynamic Systems with Special Reference to Energy Resources

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The overall objective of this research is to develop techniques for formulating and solving (optimizing) large-scale models of complex systems and to encourage their application. The research on optimization includes techniques for solving large-scale, multi-stage (staircase, dynamic) linear-programming models, their extension to take into account uncertainty, and the solving of certain classes of nonlinearly constrained (large-scale but sparse) systems such as those that arise in equilibrium analysis. The research includes methodology for complex models used to assess the effects of resource scarcity or changes technology through modernization, innovation, and computerization, and also models used for long-term planning, scheduling and control of complex engineering systems such as those that distribute energy. The research on modeling includes ways to formulate models of large complex systems; better ways to express end condition in dynamic models; ways to reformulate models more compactly; ways to use high-speed sampling methods (such as parallel processors) to simulate random events in order to formulate constraints that represent a hedge against uncertainty; and better ways to understand, maintain and update the extensive data bases and large number of constraints used to specify models. The overall objective of this research is to develop techniques for formulating and optimizing large-scale models of complex systems. The optimization techniques which will be developed will be implemented on the computer and the resulting programs will be available for use on a wide variety of large optimization problems.