论文标题
网格siphyr：一种端到端学习，以优化电力系统组合问题的框架
Grid-SiPhyR: An end-to-end learning to optimize framework for combinatorial problems in power systems
论文作者
论文摘要
混合整数问题在决策中无处不在，从离散的设备设置和设计参数，单元生产以及开/关或是/是/否决定开关，路由和社交网络中的决策。尽管存在盛行，但在具有硬约束的动态和安全至关重要的环境中，合并优化的经典优化方法对于快速，准确的决策做出了严格的速度。为了解决这一差距，我们提出了Siphyr（发音：Cipher），这是一种物理知识的机器学习框架，用于端到端学习，以优化组合问题。 Siphyr采用一种新颖的物理信息舍入方法来应对与安全关键约束的可满足性的可区分框架内组合优化的挑战。我们证明了虹吸管对清洁能源系统的新兴范式的有效性：动态重新配置，其中电网和功率流的拓扑被优化，以便在间歇性可再生生成的情况下保持安全可靠的功率网格。对代表负载和生成数据无监督框架的离线培训通过在线应用在计算上可行的网络应用来制定动态决策。
Mixed integer problems are ubiquitous in decision making, from discrete device settings and design parameters, unit production, and on/off or yes/no decision in switches, routing, and social networks. Despite their prevalence, classical optimization approaches for combinatorial optimization remain prohibitively slow for fast and accurate decision making in dynamic and safety-critical environments with hard constraints. To address this gap, we propose SiPhyR (pronounced: cipher), a physics-informed machine learning framework for end-to-end learning to optimize for combinatorial problems. SiPhyR employs a novel physics-informed rounding approach to tackle the challenge of combinatorial optimization within a differentiable framework that has certified satisfiability of safety-critical constraints. We demonstrate the effectiveness of SiPhyR on an emerging paradigm for clean energy systems: dynamic reconfiguration, where the topology of the electric grid and power flow are optimized so as to maintain a safe and reliable power grid in the presence of intermittent renewable generation. Offline training of the unsupervised framework on representative load and generation data makes dynamic decision making via the online application of Grid-SiPhyR computationally feasible.
