论文标题
分享负载:考虑使用滚动优化的降压计划中的公平性以减轻野火点火风险
Sharing the Load: Considering Fairness in De-energization Scheduling to Mitigate Wildfire Ignition Risk using Rolling Optimization
论文作者
论文摘要
野火对公共安全构成威胁,由于气候变化而导致的频率和严重程度都增加了。为了减轻野火点火风险,电力系统运营商在“公共安全电力关闭”(PSP)事件(PSPS)事件期间主动降低了高风险电源线。线路的脱水会导致社区失去权力,这可能导致负面的经济,健康和安全影响。此外,在野火季节的过程中,相同的社区可能会反复经历电力关闭,这使这些负面影响更加复杂。但是,电力线可能有许多组合,它们的降低能量会导致全系统范围的野火风险相同,但相关的停电会影响不同的社区。因此,人们可能会引起人们对脱发决定的公平性的担忧。因此,本文提出了一个框架来选择线以平衡降低野火风险,总负载和公平性考虑因素。该框架的目的是防止一小部分社区受到PSP事件影响的不成比例,而更加同等地分担停电的负担。对于美国西南部的地理分配测试用例,我们使用实际的加利福尼亚需求数据以及实际的野火风险预测来模拟2021年野火季节的PSP事件,并比较了促进公平性的各种方法的性能。我们的结果表明,所提出的配方可以提供更明显的公平结果,并且对全系统性能的影响有限。
Wildfires are a threat to public safety and have increased in both frequency and severity due to climate change. To mitigate wildfire ignition risks, electric power system operators proactively de-energize high-risk power lines during "public safety power shut-off" (PSPS) events. Line de-energizations can cause communities to lose power, which may result in negative economic, health, and safety impacts. Furthermore, the same communities may repeatedly experience power shutoffs over the course of a wildfire season, which compounds these negative impacts. However, there may be many combinations of power lines whose de-energization will result in about the same reduction of system-wide wildfire risk, but the associated power outages affect different communities. Therefore, one may raise concerns regarding the fairness of de-energization decisions. Accordingly, this paper proposes a framework to select lines to de-energize in order to balance wildfire risk reduction, total load shedding, and fairness considerations. The goal of the framework is to prevent a small fraction of communities from disproportionally being impacted by PSPS events, and to instead more equally share the burden of power outages. For a geolocated test case in the southwestern United States, we use actual California demand data as well as real wildfire risk forecasts to simulate PSPS events during the 2021 wildfire season and compare the performance of various methods for promoting fairness. Our results demonstrate that the proposed formulation can provide significantly more fair outcomes with limited impacts on system-wide performance.