Workshop Proposal: Data-Driven Energy Systems: From Data Collection to Information Technology for Sustainable Services

该项目旨在组织一次由领先的工程学、计算机科学和决策思想领袖参加的全国会议，并就未来的能源管理系统(EMS)交换意见。将特别讨论新的EMS架构的目标，以支持这样一个前提，即支持IT支持的分布式松散协调架构在飓风和龙卷风等重大计划外自然灾害期间既高效又强大。目前，EMS软件没有使电力用户能够在此类重大自然灾害期间主动应对系统状况的应用程序。也没有快速自下而上恢复的工具。研讨会将讨论下一代多层EMS体系结构可能存在的问题，这些体系结构能够激励用户群体(可以将其称为智能平衡机构(IBA))在正常情况下尽可能有效地利用资源，并具有足够的适应性和灵活性，以确保在单/双设备故障期间(公用事业公司现在通过保留保守的备用最坏情况储备来规划)以及自然灾害期间(公用事业公司没有方法确保可靠的优先服务，而不需要过多的备用储备)提供差异化的可靠服务。研讨会将探讨分布式智能和正确的IT信号以及正确的并行异步算法如何嵌入到不同的层中，从而实现社会选择和系统级性能。

This project is to organize a national conference with leading engineering, computer science and policy making thought leaders and exchange ideas on future energy management systems (EMS). The objectives of the new EMS architectures will particularly be discussed to support the premise that IT-enabled distributed loosely coordinated architecture is both efficient and robust during major unplanned natural disasters, like hurricanes and tornadoes. At present, EMS software does not have applications that enable electricity users to pro-actively respond to system conditions during such major natural disasters. There are no tools for fast bottom-up restoration, either. The workshop will address possible problem formulations for next generation multi-layered EMS architectures capable of incentivizing the groups of users (one may refer to them as the intelligent balancing authorities (iBAs)) to utilize resources as efficiently as possible during normal condition, and be sufficiently adaptive and flexible to ensure differentiated reliable service during single/double equipment outages (planned for by the utilities now by keeping conservative stand-by the worst case reserves) as well as during natural disasters (for which utilities do not have methods to ensure reliable prioritized service without requiring excessive stand-by reserve). The workshop will explore how the distributed intelligence and the right IT signals with the right parallel asynchronous algorithms embedded into different layers would enable both social choice and system-level performance.