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

该项目设想了一个新的框架，使电网运营商能够围绕灵活的需求闭合循环。研究人员将引入并测试响应需求的传递函数动态系统视图，其中响应可以是对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，对受激励和其他影响变量影响的消费者行为的动态响应进行建模，并深入了解独立系统运营商如何将需求响应充分纳入考虑。一旦成功，这一举措可能会释放当今需求响应市场尚未开发的集体灵活性。这项探索性研究可能会对未来网格中数千万接入点的系统建模、分析和控制产生变革性的影响。它将使未来的电网运营商和能源管理系统(EMS)在确定响应客户的特征方面受益。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)提交研究结果。该项目还将解决与聚合许多基于价格的需求响应相关的不确定性量化问题。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发电机建模的方式来考虑负载灵活性。因此，它使得围绕发电和需求同时进行环路的整体关闭成为可能。这个项目的智力价值有三个方面。首先，它提供了一种统一的动态系统方法来对智能电网中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，但反应需求(包括对价格反应的需求)并没有以反映电力消费显著特征的方式进行类似的建模。将发电者和需求响应都建模为动力系统，使得对发电和用电进行整体处理成为可能。其次，该项目调查了与响应需求相关的聚合不确定性。这一难题将解决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合同设计，以获得理想的需求响应水平。这项早期研究提出了一个雄心勃勃的计划，从传递函数的角度对响应性需求进行建模和控制，可能具有很高的回报。

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.