Monitoring and Optimization in Coupled Natural Gas and Electric Power Networks

对高效、低碳和快速响应的发电机的迫切需求以及天然气价格的下降正在改变天然气网络。燃气发电厂为补偿风力发电的间歇性运行，在不断增加的天然气需求量中引入了时空波动。电力传输系统中的发电和网络突发事件可能会影响天然气供应短缺;反之亦然，拥挤和次优调度的天然气网络限制了可再生能源的整合。在此背景下，本项目通过协调控制和市场运作来研究这两个关键基础设施之间的相互依赖性，以确保稳定性，可靠性和效率。该研究项目将开发用于分析，优化和监测天然气网络及其与电网相互作用的算法工具。愿景有三个方面：开发可靠的计算工具箱，利用凸优化的进步增强天然气网络运营;通过联合调度两个能源系统来识别类比、关键差异和机会;并进行数据分析，将天然气输配网络转变为更智能的网络物理基础设施。由此带来的效率和意识的提高将反映在工业、商业和住宅对天然气的广泛使用上;同时使向低排放经济的平稳过渡成为可能。更广泛的变革性影响将来自教育传播计划、本科生参与研究以及与当地社区和高中生的外联。天然气管网建模、控制和监测的核心是一组将节点气体注入和压力与管道流量相关联的非线性方程组。建立在凸松弛，气体流量问题制定为一个半定的程序，优于现有的替代品的收敛区域，自然导致耦合气体和电力流配方。新的计划是广义的优化调度天然气网络，独立或串联与电力系统。为了科普可再生能源发电的不确定性，并适应较慢的气体瞬变，通过分散和随机实现考虑静态和动态设置。此外，还设计了有效的模块，用于通过天然气网络状态估计来增强态势感知。通过开发一套天然气压力和注入数据分析，计算工作的目标将是加强当地天然气公用事业分销网络，从而与智慧城市和互联社区的努力产生共鸣。所提出的研究的效用远远超出了设想的应用领域的随机优化，统计信号处理，耦合网络的控制，推理图，流体的动态建模等更广泛的领域。

The pressing need for efficient, low-carbon, and fast-responding electric power generators and decreasing prices of natural gas are currently transforming natural gas networks. The intermittent operation of gas-fired power plants to compensate wind generation introduces spatiotemporal fluctuations in continuously increasing volumes of gas demand. Generation and network contingencies in power transmission systems can affect shortages in gas supplies; and vice versa, congested and suboptimally scheduled gas networks limit the integration of renewable energy. In this context, this project studies the interdependency between these two critical infrastructures through coordinated control and market operations to ensure stability, reliability, and efficiency. The research project will develop algorithmic tools for the analysis, optimization, and monitoring of natural gas networks and their interplay with electric power grids. The vision is threefold: develop a reliable computational toolbox for enhanced gas network operations leveraging advances in convex optimization; identify analogies, key differences, and opportunities by jointly dispatching the two energy systems; and engage data analytics for transforming both gas transmission and distribution networks into smarter cyber-physical infrastructures. The consequent gains in efficiency and awareness will be reflected on a wide gamut of industrial, commercial, and residential uses of natural gas; while enabling a smooth transition to the low-emission economy. Broader transformative impact will result from an educational dissemination plan, involvement of undergraduates in research, and outreach to the local community and high school students.At the heart of modeling, control, and monitoring of gas networks is a set of nonlinear equations relating nodal gas injections and pressures to flows over pipelines. Building on convex relaxations, the gas flow problem is formulated as a semidefinite program that outperforms existing alternatives in terms of region of convergence and which naturally leads to coupled gas and power flow formulations. The novel scheme is generalized for optimally dispatching natural gas networks, independently or in tandem with electric power systems. To cope with uncertainty on renewable generation, and adjusting to the slower gas transients, static and dynamic setups are considered via decentralized and stochastic implementations. Efficient modules are additionally devised for enhancing situational awareness via gas network state estimation. By developing a suite of gas pressure and injection data analytics, computational efforts will aim towards enhancing local gas utility distribution networks, thus resonating with efforts for smart cities and connected communities. The utility of the proposed research goes well beyond the envisioned application area to the broader fields of stochastic optimization, statistical signal processing, control of coupled networks, inference over graphs, and dynamic modeling of fluids.