Metering Placement of PMUs and Smart Meters for State Estimation in Active Distribution Grids
Due to the increasing dynamics and uncertain, changing behavior of the actors and the emergence of new control needs in distribution grids, real-time monitoring is becoming more and more important. A key tool to achieve reliable and accurate awareness of the system conditions is the distribution state estimation (DSE) based on accurate, real-time measurements. For an incremental deployment of the measurement infrastructure, the use of any available, though heterogeneous, metering devices providing aforementioned measurement data can significantly affect the DSE process for distribution grid.
In this work, we start with the study on the meter placement problem for the DSE with heterogeneous measurements including measurement data from synchronized phasor measurement units (PMUs) and Smart Meters, in addition to measurements that are typical of distribution networks, including pseudo and virtual measurements. This work aims at defining a design approach for finding the optimal measurement infrastructure for active distribution grid. The design problem is solved in terms of a stochastic optimization taking into account the accuracy requirement and the overall uncertainty of the state estimates as well as economic constraints.
The proposed method provides, for distribution grids:
• a tradeoff between accuracy of the state estimation, number of PMUs and number of Smart Metermeasurements, and hence the incremental cost of new instrumentation deployments, in distribution networks;
• a method for designing the measurement infrastructure for target accuracy of the state estimation.
This approach is validated first by applying it to a sample case study representing a significant portion of an existing distribution network and considering of different network topologies and operating conditions. The preliminary results show that the proposed approach is particularly suited for active and smart grids, since it is capable of taking into account the topological and operational changes that characterize the future dynamic management of such networks. This method can assist distribution system operators in making investment decisions to upgrade the infrastructure for the upcoming changes.
This research work is carried out in cooperation with Prof. Carlo Muscas, University of Cagliari, Italy.