In order to attain a sustainable energy future with high utilization of renewable energy, it is crucial that future power systems can utilize the flexibility of demand to compensate for the uncertainty and variability of renewable supply. However, while wholesale electricity prices fluctuate significantly over time, retail electricity rates are often set at a fixed level (i.e., the so-called flat-rate structure) in most of the U.S. Within such a flat-rate structure, entities with demand flexibility have neither incentives nor effective ways to help improve the power system's capability to cope with renewable uncertainty. To address this issue, it is envisioned that dynamic price signals will be passed to the demand-side entities, including utilities, demand aggregators, and distributed-generation/microgrid operators, with the hope that they will be incentivized to change their consumption patterns to help achieving more efficient and robust power grid operations. However, such price-driven demand-side response will in turn affect the price signals. If not designed properly, this closed-loop interaction, when coupled with renewable uncertainty, can produce highly volatile system dynamics. The resulted volatility will increase not only the risk of system instability, but also the price uncertainty and financial risk faced by consumers, ultimately discouraging them from participating in active demand response. Thus, there is a pressing need to understand at a fundamental level how to design price-driven demand response that can achieve stable, robust and efficient power system operations. By addressing this open challenge, this project develops the critically-needed system-level understanding of how to achieve robust power system operations through price-driven active demand-side participation. On a broader scale, the results of the project will contribute to the increasing adoption of renewable energy and to the smooth transition to a clean energy future. The results are also expected to contribute to control theory and game theory by advancing the design of competitive online algorithms and decentralized learning algorithms for managing renewable uncertainty. The results will be widely disseminated through publications and seminars. Further, the project team will leverage the Energy Academy program at Purdue for outreach to talented high school seniors and teachers.
This project will develop the theoretical foundations, especially in terms of control and learning algorithms, that will enable distributed generation providers, microgrid operators and demand aggregators to actively participate in price-driven demand response in a robust, stable, and efficient manner. The key novelty of the project is its formulation of the robustness and stability requirements in a rigorous mathematical framework, such that despite uncertainty in future conditions, the produced system outcome (in terms of efficiency and/or volatility) will be provably competitive compared to a carefully-chosen set of benchmark settings/algorithms. Specifically, the project addresses both regulated utilities that use pre-announced price signals and online algorithms to offset the uncertainty and variability from renewable supply and demand, and for deregulated markets where consumers' electricity rates are indexed to the ex-post ISO/RTO real-time market prices. In both cases, the project team will develop robust online control and learning algorithms that not only achieve high energy efficiency and reduce dependency on fossil fuel based generation, but also lower the volatility of the system dynamics and market dynamics to a minimum.
