Achieving Robust Power System Operations under Uncertainty and Price-Driven Active Demand-Side Participation

为了在可再生能源利用率高的情况下实现可持续能源的未来，未来的电力系统能够利用需求的灵活性来补偿可再生能源供应的不确定性和变异性是至关重要的。然而，尽管批发电价随着时间的推移而大幅波动，但在美国大部分地区，零售电价往往设定在固定的水平(即所谓的统一费率结构)。在这种统一费率结构中，具有需求灵活性的实体既没有激励措施，也没有有效的方法来帮助提高电力系统应对可再生能源不确定性的能力。为了解决这一问题，设想将动态价格信号传递给需求侧实体，包括公用事业公司、需求聚合器和分布式发电/微电网运营商，希望激励它们改变消费模式，以帮助实现更高效和更稳健的电网运营。然而，这种价格驱动的需求端反应将反过来影响价格信号。如果设计不当，这种闭环相互作用，再加上可再生的不确定性，可能会产生高度不稳定的系统动态。由此产生的波动不仅会增加系统不稳定的风险，还会增加消费者面临的价格不确定性和金融风险，最终阻碍他们参与积极的需求反应。因此，迫切需要从根本上了解如何设计价格驱动的需求响应，以实现稳定、稳健和高效的电力系统运行。通过应对这一开放的挑战，该项目发展了迫切需要的系统级的理解，即如何通过价格驱动的积极需求侧参与来实现电力系统的稳健运行。在更广泛的范围内，该项目的成果将有助于更多地采用可再生能源，并有助于平稳过渡到清洁能源的未来。研究结果还有望通过促进竞争性在线算法和分散学习算法的设计来管理可再生不确定性，从而为控制理论和博弈论做出贡献。结果将通过出版物和研讨会广泛传播。此外，项目团队将利用普渡大学的能源学院计划，向有才华的高中高年级学生和教师进行推广。该项目将发展理论基础，特别是在控制和学习算法方面，使分布式发电供应商、微电网运营商和需求聚合器能够以稳健、稳定和高效的方式积极参与价格驱动的需求响应。该项目的主要创新之处在于，它在一个严格的数学框架中提出了稳健性和稳定性要求，这样，尽管未来条件不确定，但所产生的系统结果(在效率和/或波动性方面)与精心选择的一套基准设置/算法相比，将具有明显的竞争力。具体地说，该项目既涉及使用预先宣布的价格信号和在线算法来抵消可再生供需的不确定性和变异性的受监管公用事业，也涉及消费者电价与事后ISO/RTO实时市场价格挂钩的放松管制的市场。在这两种情况下，项目组将开发强大的在线控制和学习算法，不仅实现高能效并减少对基于化石燃料的发电的依赖，还将系统动态和市场动态的波动性降低到最低限度。

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.