Multi-objective optimization under uncertainty for electrical energy systems with renewable energy sources, load participation, and plug-in hybrid electric vehicles

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Electrical Energy Systems are on the edge of a significant revolution in their operation and planning. Great interest is being directed towards increased integration of renewable or "clean" energy sources such as wind- and solar-based generation, and they are expected to be a substantial component of future electrical power systems. Power system operators and planners must address many new challenges when faced with large integration of wind- and solar-powered generation in electrical grids because of the variability associated with their power output and geographical limitations where they can be located. The challenges can lead to economic and stability problems if not properly addressed. The objectives of the proposed research program is to first develop a deeper understanding of relationships between economic cost, environment costs, and stability when incorporating stochastic elements such as wind and solar generation, and Plug-in Hybrid Electric Vehicles (PHEV). Multi-objective algorithms, appropriate for power system planning and operation, given large penetrations of stochastic elements, such as wind and solar generation and PHEV will be developed. The aim of the algorithms is to enable the development of tools that will maximize the benefit society can gain from Plug-in Hybrid Electric Vehicles and renewable energy sources such as solar and wind.