RIPS Type 2: Collaborative Research: Towards resilient computational models of electricity-gas ICI

通过使用以天然气为燃料的发电厂，电力部门是天然气（NG）增长最快的消费者之一。在美国许多地区，lng发电已经占据了电力组合的很大一部分，随着可再生能源的日益普及，由于前者具有快速、按需响应的能力，而后者具有间歇性和不确定性的特点，lng发电正在进一步增加。因此，天然气和电力网络的联系和相互依赖日益紧密。因此，电网运营商和天然气供应商之间更紧密的协调和信息共享变得非常必要，以建立一个有弹性的、相互依赖的电力和天然气关键基础设施。本提案旨在创建该基础设施的计算模型，获得对现有基础设施响应系统和政策变化的分析见解，并为具有改进弹性的协调基础设施设计指导方针。这些模型尽可能地基于来自工业合作伙伴的真实数据，并结合了新英格兰地区的电力和天然气网络的缩放版本。实现这些目标的具体工具包括安全约束单元承诺，一种为给定网络拓扑和策略提供最佳发电调度的强大工具，动态市场机制和混合网络科学，允许分析基础设施的不确定性，以及基于网络安全的恶意和非恶意事件弹性工具。

One of the fastest growing consumers of Natural Gas (NG) is the electricity sector, through the use of NG-fired power plants. Already a large portion of the electricity portfolio mix in many regions in the US, NG-fired power generation is increasing even further with growing penetration of renewable energy due to the former's fast, on-demand response capabilities, and latter's characteristics of intermittency and uncertainty. As a result, NG and electricity networks are getting increasingly linked and interdependent. Tighter coordination and information sharing between electric grid operators and NG suppliers is therefore becoming highly necessary to have a resilient interdependent critical infrastructure of electricity and NG. This proposal seeks to create computational models of this infrastructure, gain analytical insight into the existing infrastructure in response to system and policy changes, and design guidelines for a coordinated infrastructure with improved resilience. These models are based, wherever possible, on real data from industrial partners and incorporate scaled versions of the electrical and NG networks of the New England area. Specific tools to reach these objectives include security constrained unit commitment, a powerful tool that provides optimal dispatch of power generation for a given network topology and policies, dynamic market mechanisms and hybrid network science, that permit analysis of uncertainty on the infrastructure, and cyber-security based resiliency tools for malicious and non-malicious events.