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Towards Efficient Aggregation of Storage Flexibilities in Power Grids

Emrah Öztürk, Kevin Kaspar, Timm Faulwasser, Karl Worthmann, Peter Kepplinger, Klaus Rheinberger

TL;DR

This study improves upon the previously proposed vertex-based inner approximation, extending it to more general storage devices and demonstrates the efficacy and accuracy of the proposed method in a case study comparing the approach with an exact centralized control framework.

Abstract

The increasing penetration of volatile renewables combined with increasing demands poses a challenge to modern power grids. Furthermore, distributed energy resources and flexible devices (electric vehicles, PV generation, ...) are becoming more widespread, making their aggregate usage for ancillary services interesting. However, accurately quantifying the aggregate flexibility of numerous flexible devices is known to be limited by the curse of dimensionality, i.e., it does not scale well computationally. This has led to the development of various approximation algorithms. In this study, we improve upon our previously proposed vertex-based inner approximation, extending it to more general storage devices. We demonstrate the efficacy and accuracy of the proposed method in a case study comparing our approach with an exact centralized control framework, where the flexibility of numerous electric vehicles is combined to reduce the peak load in a residential area.

Towards Efficient Aggregation of Storage Flexibilities in Power Grids

TL;DR

This study improves upon the previously proposed vertex-based inner approximation, extending it to more general storage devices and demonstrates the efficacy and accuracy of the proposed method in a case study comparing the approach with an exact centralized control framework.

Abstract

The increasing penetration of volatile renewables combined with increasing demands poses a challenge to modern power grids. Furthermore, distributed energy resources and flexible devices (electric vehicles, PV generation, ...) are becoming more widespread, making their aggregate usage for ancillary services interesting. However, accurately quantifying the aggregate flexibility of numerous flexible devices is known to be limited by the curse of dimensionality, i.e., it does not scale well computationally. This has led to the development of various approximation algorithms. In this study, we improve upon our previously proposed vertex-based inner approximation, extending it to more general storage devices. We demonstrate the efficacy and accuracy of the proposed method in a case study comparing our approach with an exact centralized control framework, where the flexibility of numerous electric vehicles is combined to reduce the peak load in a residential area.
Paper Structure (5 sections, 5 equations, 4 figures, 3 algorithms)

This paper contains 5 sections, 5 equations, 4 figures, 3 algorithms.

Table of Contents

  1. Introduction
  2. Preliminaries
  3. Aggregation of Storage Devices
  4. Case Study
  5. Conclusions

Figures (4)

  • Figure 1: Example cases, in which the energy constraints in Eqs. \ref{['eq:1']} are violated
  • Figure 2: Left: extreme actions $y_1^{\left(1,1\right)},y_2^{\left(1,1\right)}$ in their polytopes, shown in dashed blue and solid green together with the summed extreme action $v^{\left(1,1\right)}$ in the Minkowski sum in dashed-dotted black. Right: all summed extreme actions $v^j, j \in \{-1,1\}^2$ with their convex hull.
  • Figure 3: Available EVs (top), total EV trip consumption (centre), and total uncontrolled EV charging (bottom).
  • Figure 4: Residential loads: the base load is shown in blue, the aggregate controlled load in orange, and the centralized controlled load in green.