| With growing concerns about environment and energy independence issues, more and more renewable energy resources such as wind and solar are expected to be integrated into the future power grid. Due to the intermittence and limited dispatch-ability of renewable generation, its large-scale integration could upset the balance between supply and demand, and affect grid reliability. To maintain grid reliability, traditional approaches include adding more operating reserves such as fast-responsive generators, which in turn incurs an increased cost and meanwhile discounts the environmental benefits of renewable generation. To combat the intermittence of renewable generation, in this thesis, an alternative solution is considered, which leverages the flexibility of energy storage and loads in grid-wide services.;With the assistance of advanced "smart grid" technologies (e.g., information technology, control, and economics), the general objective of this thesis is to facilitate the large-scale renewable integration so as to improve the long-term performance of power grids (e.g., reliability, social welfare, or cost effectiveness). In achieving this goal, several challenges are encountered, such as system uncertainty, coupling of system operational constraints, and large scale of power grids. Compared with previous works, this work builds on more complete system models that can accommodate a wide spectrum of vital characteristics of a power system. We explicitly incorporate system uncertainty (e.g., uncertainty of renewable generation, electricity price, and loads) into the problem formulation. For the control of energy storage and loads, we provide centralized algorithms that are easy to implement in reality, and at the same time ensure strong analytical performance. Furthermore, we propose distributed implementation of the centralized algorithms, which converges fast and only requires limited information exchange. |
