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

电力能源系统在运行和规划方面正处于一场重大革命的边缘。人们对增加可再生能源或“清洁”能源（如风能和太阳能发电）的一体化产生了极大的兴趣，预计它们将成为未来电力系统的重要组成部分。电力系统运营商和规划者在面对电网中风能和太阳能发电的大规模整合时，必须应对许多新的挑战，因为它们的功率输出具有可变性，而且它们所在的地理位置也有限制。如果处理不当，这些挑战可能导致经济和稳定问题。

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.