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A Plug and Play Distributed Secondary Controller for Microgrids with Grid-Forming Inverters

Vivek Khatana, Soham Chakraborty, Murti V. Salapaka

TL;DR

The paper tackles secondary control in microgrids with grid-forming IBRs by proposing a distributed optimization-based controller that achieves voltage regulation and reactive-power sharing using only local measurements and neighbor information. It leverages a GradConsensus-based procedure to solve a coupled optimization in a plug-and-play fashion, yielding an online update law that blends a shared nominal value with local measurements to realize either equal sharing or voltage regulation. Convergence is established under strongly connected communication graphs, and controller-hardware-in-the-loop experiments validate efficacy, privacy preservation, and robustness. The approach enhances scalability, privacy, and resilience in MG secondary control, especially under heterogeneous line impedances.

Abstract

A distributed controller for secondary control problems in microgrids with grid-forming (GFM) inverter-based resources (IBRs) is developed. The controller is based on distributed optimization and is synthesized and implemented distributively enabling each GFM IBR to utilize decentralized measurements and the neighborhood information in the communication network. We present a convergence analysis establishing voltage regulation and reactive power sharing properties. A controller-hardware-in-the-loop experiment is conducted to evaluate the performance of the proposed controller. The experimental results corroborate the efficacy of the proposed distributed controller for secondary control.

A Plug and Play Distributed Secondary Controller for Microgrids with Grid-Forming Inverters

TL;DR

The paper tackles secondary control in microgrids with grid-forming IBRs by proposing a distributed optimization-based controller that achieves voltage regulation and reactive-power sharing using only local measurements and neighbor information. It leverages a GradConsensus-based procedure to solve a coupled optimization in a plug-and-play fashion, yielding an online update law that blends a shared nominal value with local measurements to realize either equal sharing or voltage regulation. Convergence is established under strongly connected communication graphs, and controller-hardware-in-the-loop experiments validate efficacy, privacy preservation, and robustness. The approach enhances scalability, privacy, and resilience in MG secondary control, especially under heterogeneous line impedances.

Abstract

A distributed controller for secondary control problems in microgrids with grid-forming (GFM) inverter-based resources (IBRs) is developed. The controller is based on distributed optimization and is synthesized and implemented distributively enabling each GFM IBR to utilize decentralized measurements and the neighborhood information in the communication network. We present a convergence analysis establishing voltage regulation and reactive power sharing properties. A controller-hardware-in-the-loop experiment is conducted to evaluate the performance of the proposed controller. The experimental results corroborate the efficacy of the proposed distributed controller for secondary control.
Paper Structure (10 sections, 2 theorems, 20 equations, 3 figures, 1 table)

This paper contains 10 sections, 2 theorems, 20 equations, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Brief Description of Secondary Control
  3. Distributed Reactive Power Sharing Scheme
  4. Control/Update Rule Design
  5. GradConsensus Method
  6. Solution to Problem \ref{['eq:opt_prob']}
  7. CHIL Demonstration and Results
  8. Experimental Configuration
  9. Results and Discussions
  10. Conclusion and Future Work

Key Result

Lemma 1

Let graph $\mathcal{G}$ be strongly connected (i.e. $\mathcal{G}$ has a directed path between every pair of distinct nodes). Then, the solution $x_\mathrm{t}^*$ for problem eq:opt_prob is $x_\mathrm{t}^*:= \frac{1}{N(1+\gamma)} \sum_{i=1}^N \alpha_\mathrm{i}(t)$.

Figures (3)

  • Figure 1: (a) Distributed $\textit{secondary~control}$ architecture with $\textit{primary~control}$ for multiple GFM IBRs connected in parallel, (b) $P$$\sim$$f$, $Q$$\sim$$V$ droop-based $\textit{primary~control}$ with $\textit{secondary~control}$ for parallel GFM IBRs $i$ and $j$ with equal ratings, connected to PCC via reactive lines with $X_\mathrm{j} > X_\mathrm{i}$.
  • Figure 2: The laboratory-based system-in-the-loop (SIL) and controller hardware-in-the-loop (CHIL) experimental hardware setup.
  • Figure 3: Performance results of the developed distributed secondary controller. (a) Under $\mathtt{CASE}$-$1$: equal-rated reactive power sharing, (b) Under $\mathtt{CASE}$-$2$: tight voltage regulation, (c) Under $\mathtt{CASE}$-$3$: mixed objective DERs $1$-$7$ equal-rated reactive power sharing and DERs $8$-$10$ tight voltage regulation.

Theorems & Definitions (8)

  • Lemma 1
  • proof
  • Remark 1
  • Remark 2
  • Remark 3
  • Theorem 1
  • proof
  • Remark 4