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

为了实现可持续能源的未来与可再生能源的高利用率，这是至关重要的，未来的电力系统可以利用需求的灵活性，以补偿可再生能源供应的不确定性和可变性。然而，虽然批发电价随时间大幅波动，但零售电价通常设定在固定水平（即，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也没有有效的方法来帮助提高电力系统科普可再生能源不确定性的能力。为了解决这个问题，可以设想，动态价格信号将传递给需求方实体，包括公用事业，需求聚合器和分布式发电/微电网运营商，希望他们能够激励他们改变消费模式，以帮助实现更高效和更强大的电网运营。然而，这种价格驱动的需求方反应反过来又会影响价格信号。如果设计不当，这种闭环相互作用与可再生的不确定性相结合，可能会产生高度波动的系统动态。由此产生的波动不仅会增加系统不稳定的风险，而且会增加消费者面临的价格不确定性和金融风险，最终使他们不愿参与积极的需求应对。因此，迫切需要在基本层面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。通过解决这一开放性挑战，该项目开发了如何通过价格驱动的主动需求方参与实现强大的电力系统运行的迫切需要的系统级的理解。 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，并有助于向清洁能源的未来平稳过渡。 研究结果还有望通过推进竞争性在线算法和分散学习算法的设计来管理可再生的不确定性，从而为控制理论和博弈论做出贡献。调查结果将通过出版物和研讨会广泛传播。此外，该项目团队还将利用普渡大学能源学院的项目，为有才华的高中毕业生和教师提供服务。该项目将开发理论基础，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商能够以稳健、稳定和高效的方式积极参与价格驱动的需求响应。该项目的关键新奇在于其在严格的数学框架中制定了鲁棒性和稳定性要求，因此，尽管未来条件存在不确定性，但与精心选择的基准设置/算法相比，所产生的系统结果（在效率和/或波动性方面）将具有可证明的竞争力。具体而言，该项目既涉及使用预先公布的价格信号和在线算法来抵消可再生能源供需的不确定性和可变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市场。在这两种情况下，项目团队将开发强大的在线控制和学习算法，不仅实现高能效，减少对化石燃料发电的依赖，还将系统动态和市场动态的波动性降至最低。

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.