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Article overview
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Scalable Optimal Design of Incremental Volt/VAR Control using Deep Neural Networks | Sarthak Gupta
; Ali Mehrizi-Sani
; Spyros Chatzivasileiadis
; Vassilis Kekatos
; | Date: |
4 Jan 2023 | Abstract: | Volt/VAR control rules facilitate the autonomous operation of distributed
energy resources (DER) to regulate voltage in power distribution grids.
According to non-incremental control rules, such as the one mandated by the
IEEE Standard 1547, the reactive power setpoint of each DER is computed as a
piecewise-linear curve of the local voltage. However, the slopes of such curves
are upper-bounded to ensure stability. On the other hand, incremental rules add
a memory term into the setpoint update, rendering them universally stable. They
can thus attain enhanced steady-state voltage profiles. Optimal rule design
(ORD) for incremental rules can be formulated as a bilevel program. We put
forth a scalable solution by reformulating ORD as training a deep neural
network (DNN). This DNN emulates the Volt/VAR dynamics for incremental rules
derived as iterations of proximal gradient descent (PGD). Analytical findings
and numerical tests corroborate that the proposed ORD solution can be neatly
adapted to single/multi-phase feeders. | Source: | arXiv, 2301.01440 | Services: | Forum | Review | PDF | Favorites |
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