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

在不确定性和价格驱动的需求侧积极参与下实现电力系统的稳健运行

• 批准号: 1509536
• 负责人: Xiaojun Lin
• 金额: $ 39.98万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1509536&HistoricalAwards=false
• 关键词: Achieving; Robust; Power; System; Operations

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.

为了通过高利用可再生能源实现可持续的能源未来，至关重要的是，未来的电力系统可以利用需求的灵活性来弥补可再生供应的不确定性和可变性。但是，尽管随着时间的流逝，零售电力率通常会在美国大多数美国大多数的固定水平（即所谓的扁平率结构）设置为零售电力率显着波动，但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改善电源系统能够应对可恢复可恢复的不确定性。为了解决这个问题，可以预见，动态价格信号将被传递给需求端实体，包括公用事业，需求聚合器和分布式生成/微电网运营商，希望他们能激励他们改变其消耗模式以帮助实现更有效和强大的电力电网操作。但是，这种以价格驱动的需求方响应将反过来影响价格信号。如果设计不当，则这种闭环相互作用（与可再生不确定性结合在一起）会产生高度挥发性的系统动力学。由此产生的波动性不仅会增加系统不稳定性的风险，而且还会增加消费者面临的价格不确定性和财务风险，最终阻止他们参与积极的需求响应。因此，迫切需要在基本层面上理解如何设计以价格驱动的需求响应，以实现稳定，健壮和有效的电源系统操作。通过应对这一开放挑战，该项目开发了急需的系统级别的理解，即如何通过价格驱动的主动需求端参与来实现强大的电力系统操作。 在更广泛的范围内，该项目的结果将有助于不断增加的可再生能源采用以及平稳过渡到清洁能源的未来。 预计该结果将通过推进竞争性在线算法的设计和分散的学习算法来管理可再生不确定性，从而为控制理论和游戏理论做出贡献。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才华横溢的高中生和老师提供宣传。该项目将开发理论基础，尤其是在控制和学习算法方面，该项目将使分布式生成提供商，微射流运营商和需求汇总能够积极参与价格驱动的需求响应，以稳健的需求和良心的态度，并提供良好的态度。该项目的关键新颖性是它在严格的数学框架中对鲁棒性和稳定性要求的制定，使得与精心调整的基准标准设置/算法相比，与精心调整的集合相比，尽管将来的条件不确定，但在未来的条件下不确定性（就效率和/或波动率而言）而言。具体而言，该项目涉及使用预宣布的价格信号和在线算法的受监管的公用事业，以抵消可再生供应和需求的不确定性和可变性，以及对于消费者的电力率，将消费者的电费索引到EX-POST ISO/RTO实时市场价格。在这两种情况下，项目团队都将开发出强大的在线控制和学习算法，这些算法不仅可以实现高能源效率并降低对基于化石燃料的生成的依赖，而且还将系统动态和市场动态的波动降低到最低。

项目成果

Competitive Online Convex Optimization With Switching Costs and Ramp Constraints

• DOI: 10.1109/tnet.2021.3053910
• 发表时间: 2021-02
• 期刊: IEEE/ACM Transactions on Networking
• 作者: Ming Shi;Xiaojun Lin;S. Fahmy