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Marco Pau

Publications and source records attributed to Marco Pau.

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Designing Active Operation in Low-Voltage Distribution Grids: Requirements, Interfaces and Roadmap

This paper outlines a pathway towards active operation of lowvoltage distribution grids. In these grids, the growing deployment of distributed generation, controllable demand and storage, together with the roll-out of intelligent metering systems, creates new requirements and opportunities for distribution system operators. On the basis of the German and European regulation, and in particular of recent directives enabling grid-oriented interventions and market-based procurement of flexibility, the paper identifies three key pillars for active low-voltage operation: (a) measurement placement and observability, (b) secure and interoperable information and communication architectures and interfaces, and (c) integration of market-based and gridoriented optimisation for controlling connected assets. A structured system overview is developed that specifies main actors and data flows, highlighting central research topics across these pillars. Building on this, a four-phase roadmap is presented, spanning requirements and use-case definition, method development and simulation, laboratory and field validation, and roll-out with system-level feedback, thus providing guidance for distribution system operators and researchers.

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Distributed Robust State Estimation for Hybrid AC/DC Distribution Systems using Multi-Source Data

Hybrid AC/DC distribution systems are becoming a popular means to accommodate the increasing penetration of distributed energy resources and flexible loads. This paper proposes a distributed and robust state estimation (DRSE) method for hybrid AC/DC distribution systems using multiple sources of data. In the proposed distributed implementation framework, a unified robust linear state estimation model is derived for each AC and DC regions, where the regions are connected via AC/DC converters and only limited information exchange is needed. To enhance the estimation accuracy of the areas with low measurement coverage, a deep neural network (DNN) is used to extract hidden system statistical information and allow deriving nodal power injections that keep up with the real-time measurement update rate. This provides the way of integrating smart meter data, SCADA measurements and zero injections together for state estimation. Simulations on two hybrid AC/DC distribution systems show that the proposed DRSE has only slight accuracy loss by the linearization formulation but offers robustness of suppressing bad data automatically, as well as benefits of improving computational efficiency.

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