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Data-Driven Decision Making for Enhancing Small-Signal Stability in Hybrid AC/DC Grids Through Converter Control Role Assignment

Francesca Rossi, Sergi Costa Dilme, Josep Arevalo-Soler, Eduardo Prieto-Araujo, Oriol Gomis-Bellmunt

Abstract

Hybrid AC/DC transmission grids incorporate Modular Multilevel Converters functioning as Interconnecting Power Converters (IPCs). The control role assigned to each converter significantly influences grid dynamics. Traditionally, these converters operate with static control roles, but recent studies have proposed scheduling their roles based on day-ahead forecasts to enhance stability performance. However, in systems with high renewable energy penetration, forecast deviations can render scheduled control assignments suboptimal or even lead to instability. To address this challenge, this work proposes an online scheduling recalculation algorithm that dynamically adapts IPC control roles during system operation. The approach leverages a data-driven multi-criteria decision-making framework, integrating surrogate models of conventional small-signal stability analysis tools to enable a fast computation of system stability and stability performance indicators.

Data-Driven Decision Making for Enhancing Small-Signal Stability in Hybrid AC/DC Grids Through Converter Control Role Assignment

Abstract

Hybrid AC/DC transmission grids incorporate Modular Multilevel Converters functioning as Interconnecting Power Converters (IPCs). The control role assigned to each converter significantly influences grid dynamics. Traditionally, these converters operate with static control roles, but recent studies have proposed scheduling their roles based on day-ahead forecasts to enhance stability performance. However, in systems with high renewable energy penetration, forecast deviations can render scheduled control assignments suboptimal or even lead to instability. To address this challenge, this work proposes an online scheduling recalculation algorithm that dynamically adapts IPC control roles during system operation. The approach leverages a data-driven multi-criteria decision-making framework, integrating surrogate models of conventional small-signal stability analysis tools to enable a fast computation of system stability and stability performance indicators.

Paper Structure

This paper contains 32 sections, 12 equations, 12 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Small-signal Stability and Small-signal Stability Performance Indicators
  3. Conventional Exact Models
  4. Data-driven Surrogate Models
  5. Data-driven MCDM for CCR Assignment
  6. Problem Alternatives
  7. Problem Criteria and Performances
  8. Complete Performance Matrix and Solution
  9. Solution Verification
  10. Training The Data-driven Surrogate Models
  11. Select a Reduced Set of CCRCs Feasible Alternatives
  12. Data Generation
  13. Features Engineering
  14. Creating New Features
  15. Data Cleaning
  16. ...and 17 more sections

Figures (12)

  • Figure 1: Workflow of the data-driven MCDM algorithm
  • Figure 2: Sets intersection visualization and CCRCs selection.
  • Figure 3: Hybrid AC/DC grid test system 10017135.
  • Figure 4: CCRCs selection trough sets intersection visualization.
  • Figure 5: Stability maps considering all CCRCs (left) and a reduced set with better performance (top right) based on $\mathcal{H}_{2,V_{DC}}$. The bottom right panel presents a box plot comparing the distribution of the indicator.
  • ...and 7 more figures

Theorems & Definitions (6)

  • Definition 1
  • Definition 2
  • Definition 3
  • Definition 4
  • Definition 5
  • Definition 6