4.7 Article

Reverse and Forward Engineering of Local Voltage Control in Distribution Networks

Journal

IEEE TRANSACTIONS ON AUTOMATIC CONTROL
Volume 66, Issue 3, Pages 1116-1128

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TAC.2020.2994184

Keywords

Voltage control; Optimization; Heuristic algorithms; Convergence; Power system dynamics; Reactive power; Reverse engineering; Distributed control and optimization; network dynamics as optimization algorithms; power networks; reverse and forward engineering; voltage regulation

Funding

  1. National Renewable Energy Laboratory [APUP UGA-0-41026-120, APUP UGA-0-41026-107]
  2. National Science Foundation [CCF 1637598, ECCS 1619352, CPS ECCS 1739355]
  3. DOE [DE-EE-0007998]

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The article investigates the impact of renewable and distributed energy resources on distribution networks, proposes two local voltage control schemes to solve an optimization problem, and highlights the limitations of nonincremental local voltage control.
The increasing penetration of renewable and distributed energy resources in distribution networks calls for real-time and distributed voltage control. In this article, we investigate local Volt/VAR control with a general class of control functions, and show that the power system dynamics with nonincremental local voltage control can be seen as a distributed algorithm for solving a well-defined optimization problem (reverse engineering). The reverse engineering further reveals a fundamental limitation of the nonincremental voltage control: the convergence condition is restrictive and prevents better voltage regulation at equilibrium. This motivates us to design two incremental local voltage control schemes based on the subgradient and pseudo-gradient algorithms, respectively, for solving the same optimization problem (forward engineering). The new control schemes decouple the dynamical property from the equilibrium property, and have much less restrictive convergence conditions. This article presents another step toward developing a new foundation-network dynamics as optimization algorithms-for distributed real-time control and optimization of future power networks.

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