CyberSEES: Type 1: Data-driven approaches to managing uncertain load control in sustainable power systems

将高渗透率的可再生能源整合到电力系统中，需要额外的后备能力(储备)来管理实时电力失衡。负荷控制可以提供备用，可能比大多数发电厂的成本更低和/或对环境的影响更小。然而，由于不确定性，调度基于负荷的储备是具有挑战性的--储备的可用性是包括天气和负荷使用模式在内的随机因素的函数。因此，该研究项目正在研究数据驱动的、无分布的方法来管理电力系统调度问题中的负荷控制不确定性。通过推广分布式稳健优化算法，机会约束最优潮流求解技术被开发来管理与负荷控制相关的时变、相关和复杂的不确定性。此外，这些新技术可以用来确定负荷控制成为一种有竞争力的选择的条件。具体地说，正在量化负荷控制不确定性和盈利能力之间的权衡，以评估不确定性对环境可持续性的影响。通过这项工作开发的方法为量化对储量使用不确定负荷控制对环境的净影响奠定了基础。特别是，它们能够更明智地利用负荷控制，以支持可再生能源的整合。其结果将指导负荷控制程序设计、电力系统市场设计，以及更广泛地说，寻求平衡成本和环境影响的能源政策。此外，本文提出的优化方法同样适用于其他电力系统问题，也适用于其他领域的随机问题。

Integrating high penetrations of renewable energy resources into electric power systems requires additional back-up capacity (reserves) to manage real-time power imbalance. Load control can provide reserves, possibly at lower cost and/or with less environmental impact than most power plants. However, scheduling load-based reserves is challenging because of uncertainty -- the availability of reserves is a function of stochastic factors including weather and load usage patterns. This research project is therefore investigating data-driven, distribution-free approaches to managing load control uncertainty in power system scheduling problems. By generalizing distributionally robust optimization algorithms, chance-constrained optimal power flow solution techniques are being developed to manage the time-varying, correlated, and complex uncertainty associated with load control. Furthermore, these new techniques can be used to determine conditions under which load control becomes a competitive option. Specifically, trade-offs between load control uncertainty and profitability are being quantified in order to assess the impact of uncertainty on environmental sustainability. The methods being developed through this work form a basis for quantifying the net environmental impact of using uncertain load control for reserves. In particular, they enable more informed utilization of load control for supporting the integration of renewable energy resources. The results will guide load control program design, power system market design, and, more broadly, energy policy that seeks to balance cost and environmental impact. Furthermore, the optimization approaches being developed in this work are applicable to other power system problems, and to stochastic problems in other fields.