Scalable and Efficient Aggregation of Energy-Constrained Flexible Loads

TL;DR

This paper introduces a scalable, conservative approximation method for aggregating energy-constrained flexible loads, enabling efficient computation of group flexibility with accuracy comparable to existing techniques.

Contribution

It proposes a novel worst-case energy dispatch-based approximation for aggregated load flexibility, reducing computational costs significantly.

Findings

Efficiently computes aggregated flexibility for thousands of loads.

Provides conservative bounds based on previous energy consumption.

Achieves accuracy comparable to existing approximation methods.

Abstract

Loads represent a promising flexibility source to support the integration of renewable energy sources, as they may shift their energy consumption over time. By computing the aggregated flexibility of power and energy-constrained loads, aggregators can communicate the group's flexibility without sharing individual private information. However, this computation is, in practice, challenging. Some studies suggest different inner approximations of aggregated flexibility polytopes, but all suffer from large computational costs for realistic load numbers and horizon lengths. In this paper, we develop a novel approximation of the aggregated flexibility of loads based on the concept of worst-case energy dispatch, i.e., if aggregated energy consumptions are assumed to be dispatched in the worst manner possible. This leads to conservative piecewise linear bounds that restrict the aggregated energy consumption only based on the previous aggregated energy consumed. A comparative case study reveals that our method can compute an approximation of the aggregation of thousands of loads efficiently, while displaying an accuracy comparable to other approximation techniques.