Data strategy for active distribution networks: a framework to quantify data granularity impact on cyber-physical planning and operation

Abstract

The operational challenges of the integration of electric vehicles (EV), air conditioning and photovoltaic panels (PV) are prompting the upgrade of distribution grids, seen here as cyber-physical infrastructures. An important upgrading feature of the cyber-side is the electrical grid monitoring, which needs to expand both in data coverage and granularity. The challenge is to decide the data strategy, or in other words, which level of granularity is actually needed in active distribution networks. This work proposes a framework to assist grid planners in selecting the level of data expansion needed, by quantifying the impact of extended data granularity on control capabilities, and corresponding grid performance. The framework combines machine learning with AC optimal power flow and state estimation to select incremental upgrades of the cyber-physical infrastructure. Grid planning and operation are simulated and tested for the IEEE 33-bus test system over a 5-year span to assess the role of granularity in grid performance for different cyber-infrastructures. The results show that extending data granularity is critical for mitigating voltage violations under high penetration of EVs, air conditioning and PVs. By modeling the relationships between data, grid planning and operation, and grid performance, the framework supports efficient cyber system upgrades to mitigate operational violations while accounting for budget limitations.