The electric power system is a highly complex, interacting system that manages to balance demand and supply on very short time-scales, despite inherent variability and uncertainty on both sides of this balance. While use of a significant level of renewable energy is crucial to addressing issues of climate change, addition of these unreliable sources exacerbates the complexity of system operations. Effective use of consumer-side resources in power system operations and markets will allow the maximum use of variable renewable resources, leading to beneficial economic and environmental outcomes for society as a whole. In order to address the inclusion of demand-side resources effectively in the future grid, the response reliability of this resource must be both understood and included in power system operations and decision-making. The goal of this research is the development of an efficient and effective framework for integrated use of demand-side and renewable resources in the evolving power system. The project will also develop interactive activities to facilitate introduction of systems thinking into k-12 classrooms, in support of the Next Generation Science Standards.
A primary challenge of adding additional uncertain resources to the power systems lies in the increase in dimensionality and complexity of the existing optimization models used for system planning and dispatch. The outcomes of this project are transformative because they provide a robust and truly representative framework integrating uncertain resources on both the demand and supply side, at the fundamental operational time frames of the power system and markets. These goals will be achieved through three primary aims 1) Build the tools to accurately describe intermittent renewable resources, such as wind and solar, to better characterize the uncertainties and interactions for tractable representation in stochastic optimization methods, 2) Characterize responsive demand resources across multiple time scales including characteristics, constraints and uncertainties of various classes, and 3) Develop the multi-period stochastic optimization framework that includes DR into a scalable security constrained unit commitment model by including dispatchable DR resources as stochastic constraints, with additional constraints on frequency and duration of response, and to ensure that the solutions generated are available in reasonable time frames to benefit power system operations.
The proposed project will support the evolution to the Smart Grid through provision of analytical tools to manage increasing complexity, and to understand the synergies of new technologies. The educational contribution of this project has components designed to enhance participation of women and under-represented minorities in the field, and build excitement about computational applications in energy systems among graduate students. In addition, this project will reach nearly 100 science classrooms in the Northeast US through collaborative development and delivery of teacher professional development and classroom materials supporting systems thinking and modeling, a new cross-cutting theme in the Next Generation Science Standards.
