Analysis of Data Value in Stochastic Optimal Power Flow for Distribution Systems

The rise of advanced data technologies in electric power distribution systems enables operators to optimize operations but raises concerns about data security and consumer privacy. Resulting data protection mechanisms that alter or obfuscate datasets may invalidate the efficacy of data-driven decision-support tools and impact the value of these datasets to the decision-maker. This paper derives tools for distribution system operators to enrich data-driven operative decisions with information on data quality and, simultaneously, assess data usefulness in the context of this decision. To this end, we derive an AC optimal power flow model for radial distribution systems with data-informed stochastic parameters that internalize a data quality metric. We derive a tractable reformulation and discuss the marginal sensitivity of the optimal solution as a proxy for data value. Our model can capture clustered data provision, e.g., from resource aggregators, and internalize individual data quality information from each data provider. We use the IEEE 33-bus and IEEE 123-bus test systems, examining scenarios with varying photovoltaic penetration and load scenarios, to demonstrate the application of our approach and discuss the relationship between data quality and its value.