Data-Driven Local Control Design for Active Distribution Grids Using Off-Line Optimal Power Flow and Machine Learning Techniques
File(s)
Journal
IEEE Transactions on Smart Grid
Date Issued
November 2, 2019
Abstract
The optimal control of distribution networks often requires monitoring and
communication infrastructure, either centralized or distributed. However, most
of the current distribution systems lack this kind of infrastructure and rely
on suboptimal, fit-and-forget, local controls to ensure the security of the
network. In this paper, we propose a data-driven algorithm that uses historical
data, advanced optimization techniques, and machine learning methods, to design
local controls that emulate the optimal behavior without the use of any
communication. We demonstrate the performance of the optimized local control on
a three-phase, unbalanced, low-voltage, distribution network. The results show
that our data-driven methodology clearly outperforms standard industry local
control and successfully imitates an optimal-power-flow-based control.
communication infrastructure, either centralized or distributed. However, most
of the current distribution systems lack this kind of infrastructure and rely
on suboptimal, fit-and-forget, local controls to ensure the security of the
network. In this paper, we propose a data-driven algorithm that uses historical
data, advanced optimization techniques, and machine learning methods, to design
local controls that emulate the optimal behavior without the use of any
communication. We demonstrate the performance of the optimized local control on
a three-phase, unbalanced, low-voltage, distribution network. The results show
that our data-driven methodology clearly outperforms standard industry local
control and successfully imitates an optimal-power-flow-based control.
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