EAGER: A Dynamical Systems Approach to Modeling and Controlling Responsive Demand in Electric Power Systems

该项目设想了一种新颖的框架，使电网运营商能够围绕灵活的需求形成闭环。研究人员将引入并测试响应需求的传递函数动态系统观点，其中响应可以是价格或其他可能影响需求响应的变量，如温度。该项目将提供一个新的视角来建模受激励和其他影响变量影响的消费者行为的动态响应，以及对独立系统运营商如何充分考虑需求响应的见解。一旦成功，这一举措将释放当前需求响应市场尚未开发的集体灵活性。探索性研究可能会对未来电网中数千万接入点的系统建模、分析和控制产生变革性影响。它将使未来的电网运营商和能源管理系统（EMS）在响应客户方面受益。该团队将继续与当地电网运营商合作，并将通过实地考察向德克萨斯州电力可靠性委员会（ERCOT）提交研究成果。该项目还将解决与汇总许多基于价格的需求响应相关的不确定性的量化问题。这种建模方法将使系统操作员和负载服务实体能够以类似于当前对生成器建模的方式考虑负载灵活性。因此，它使同时围绕发电和需求的循环整体封闭成为可能。这个项目的智力价值是三重的。首先，它提供了一种统一的动态系统方法来对智能电网的需求和供应进行建模。虽然大多数发电机都很好地体现了第一原则，但响应需求（包括对价格有响应的需求）并没有以类似的方式反映电力消耗的显著特征。将发电机和需求响应建模为动态系统使得对发电和用电的整体处理成为可能。其次，本项目研究了与响应性需求相关的总体不确定性。这一难题将涉及与不同需求相关的空间和时间相关性。第三，将研究一种新的负荷服务实体的最优契约设计，以获得理想的需求响应水平。这个早期阶段的研究提出了一个雄心勃勃的、潜在的高回报的计划，从传递函数的角度来建模和控制响应性需求。

This project envisions a novel framework that will enable power grid operators to close the loop around flexible demand. The researchers will introduce and test a transfer function dynamical systems view of responsive demand, where the response can be to price or other variables that can influence demand response, such as temperature. The project will provide a fresh perspective on modeling the dynamical response of consumer behavior subject to incentives and other influence variables, as well insights into how independent system operators can incorporate demand response fully into consideration. Once successful, this initiative could unlock the collective flexibility untapped in today's demand response markets. The exploratory research is likely to have a transformative impact on the systematic modeling, analysis, and control of tens of millions of access points in the future grid. It will benefit future grid operators and Energy Management Systems (EMS) in characterizing responsive customers. The team will continue its collaboration with the local grid operator, and will present research findings to Electric Reliability Council of Texas (ERCOT) through site visits.The project will also address quantification of uncertainty associated with aggregating many price-based demand responses. This modeling approach will enable system operators and load serving entities to consider load flexibility in a manner similar to how generators are currently modeled. Thereby it makes possible a holistic closure of loops simultaneously around generation and demand. The intellectual merit of this project is three-fold. First, it offers a unifying dynamical systems approach to modeling both demand as well as supply in the smart grid. While most generators are well characterized by first principles, responsive demand (including demand that is responsive to price) has not been similarly modeled in a manner that reflects the salient feature of electricity consumption. Modeling both genrators and demand response as dynamical systems makes possible a holistic treatment of electricity generation and consumption. Second, this project investigates the aggregated uncertainty associated with responsive demand. This difficult problem will address both the spatial and temporal correlations associated with different demands. Third, a novel optimal contract design for load serving entities will be investigated, to elicit a desirable level of demand response. This early-stage research puts forward an ambitious and potentially highly rewarding plan for modeling and control of responsive demand from a transfer function perspective.