Stochastic Geometry-Based Model for Dynamic Allocation of Metering Equipment in Spatio-Temporal Expanding Power Grids

Abstract

With smart grids replacing conventional power grids and rapidly expanding in both space and time, ensuring an acceptable system observability becomes a challenge in spatio-temporal expanding power grids. In addition, system operators face another challenge, namely, financial budget constraints. To address these challenges, a metering equipment allocation strategy for monitoring of the power grid state needs to be dynamic in both space and time. Unfortunately, existing metering allocation strategies are quite limited. They usually deal with static power grid topologies, and hence, do not reflect the spatio-temporal expansion of the power grid. In this paper, a spatio-temporal power grid model is proposed based on stochastic geometry, which we show that it is in a good match with real-world power grids. The proposed model enables us to carry out tractable dynamic allocation of metering equipment in a large (city-wide) and structurally evolving power grid. Using the developed model, a multi-year algorithm for the allocation of metering equipment is proposed based on finite horizon dynamic programming, given budgetary and technical constraints on system observability. Several case studies for metering allocation are demonstrated through simulation results.