Details

Autor(en) / Beteiligte

• Hossain, Ramij Raja
• Kumar, Ratnesh

Titel

A Distributed-MPC Framework for Voltage Control Under Discrete Time-Wise Variable Generation/Load

Ist Teil von

• IEEE transactions on power systems, 2024-01, Vol.39 (1), p.1-12

Ort / Verlag

New York: IEEE

Erscheinungsjahr

2024

Quelle

IEEE Xplore

Beschreibungen/Notizen

• This article presents a distributed predictive design for real-time voltage control under changing load and generation profiles at discrete intervals. The existing control designs include centralized approach, providing an optimal solution but less scalable and susceptible to single-point failures/attacks, as well as decentralized or localized approach, having increased scalability and attack resilience but lacking optimality. The proposed distributed solution offers the attractive features of both approaches, where the neighboring nodes share their local information to attain an optimal solution while retaining scalability and resilience to single-point failures/attacks. We first introduce the centralized version of the voltage control problem assuming grid observability and then transfer it to the distributed versions based on both bus-wise and area-wise decompositions of the network. The distributed version is solved via alternating direction method of multipliers (ADMM) that, for bus-wise decomposition, needs a full set of local measurements, whereas only a partial set of local measurements (that guarantee area-wise grid observability for each area) is needed for area-wise decomposition, along with neighbor-to-neighbor communications. Additionally, leveraging the availability of measurement data, the framework includes a distributed method to estimate the admittance matrix <inline-formula><tex-math notation="LaTeX">\mathbf {Y}</tex-math></inline-formula> of the underlying network graph. The proposed framework is validated against IEEE-30, IEEE-57 bus transmission systems, and IEEE-123 bus distribution systems and can tolerate certain levels of generation/load prediction uncertainties, modeling errors, and communication failures; plus, its in-built redundancy supports attack detection.