EAGER: Causal Theory of Residential Electricity Consumption and Production: Unveiling Full Scale Demand Side Flexibility

该项目旨在加强居民与智能能源系统的互动，并使所有人都能从智能电网的新机遇中受益。分布式能源，例如，屋顶太阳能光伏系统和智能恒温器等灵活的需求方资产为居民提供了新的机会。与传统的电力系统资源不同，许多新兴的智能能源技术位于居民的住所，其参与程度取决于许多与人类相关的因素。这个NSF项目提出了新的策略来发现和解释分布式能源的关键潜在人在回路中的因素。该项目将通过实现智能电网资源的社会意识设计和运营带来变革，为居民提供广泛的财务和能源弹性效益。了解居民对智能能源系统行为的因果关系，可以更有效地设计电力公司的客户计划，增强零售电力市场设计，并使所有分布式能源资源得到更有效的利用。这些知识将通过因果学习和分析消费者参与智能电网运营来实现。该项目的智力价值包括设计创新方法，以捕捉居民对能源资源的行为及其参与能源系统平衡的关键组成部分。该项目更广泛的影响包括有效利用所有网格边缘资源。通过采取全面的方法，明确考虑社会，行为，技术和工程方面的相互作用，这项研究的成果将跨越多个学科。社会意识和行为意识的智能电网解决方案的设计是实现不同居民的可靠和广泛参与智能电网实践的关键一步，从而最大限度地利用分布式能源资源。拟议项目将采用基于人工智能算法的创新方法，对居民对新兴智能能源系统的行为进行因果分析。人类与能源相互作用的复杂性依赖于许多因素，理解行为因果关系是一个核心和未解决的挑战。该项目通过更好地利用所有资源，在建立下一代电力系统运营战略方面取得了有意义的进展。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This NSF project aims to enhance residents’ interactions with smart energy systems and empower all to benefit from new opportunities of smart electric grids. Distributed energy resources, e.g., rooftop solar photovoltaic (PV) systems and flexible demand-side assets, such as smart thermostats, provide new opportunities for residents. Unlike conventional power system resources, many emerging smart energy technologies are located at the residents’ premises and their level of participation depends on many human-related factors. This NSF project proposes novel strategies to discover and account for critical underlying human-in-the-loop factors of distributed energy resources. The project will bring transformative change by enabling socially-aware design and operation of smart grid resources, which provides a wide range of financial and energy resilience benefits to residents. Understanding causality of resident behavior towards smart energy systems enables more effective design of customer programs for electric utilities, enhances retail electricity market design, and empowers more effective utilization of all distributed energy resources. This knowledge will be achieved by causal learning and analysis of consumer participation in smart grid operations. The intellectual merits of the project include design of innovative approaches to enable capturing critical components of residents’ behavior towards energy resources and their participation in energy system balancing. The broader impacts of the project include enabling effective utilization of all grid edge resources. By taking a holistic approach, which explicitly considers the interplay of social, behavioral, technological, and engineering aspects, the outcomes of this research will span multiple academic disciplines.The design of socially-aware and behavior-aware smart grid solutions is the critical step to achieve dependable and widespread participation of diverse residents in smart grid practices leading to maximum utilization of distributed energy resources. The proposed project will pursue innovative methods based on artificial intelligence algorithms for causal analysis of residents’ behavior towards emerging smart energy systems. The complex nature of human interactions with energy relies on many factors and understanding behavior causality is a core and unsolved challenge. This project makes meaningful inroads towards establishing the next generation of power systems operational strategies by enabling better utilization of all resources.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.