Data-Driven Modeling of Aggregate Flexibility under Uncertain and Non-Convex Load Models

TL;DR

This paper introduces a data-driven method for aggregators to design optimal energy flexibility models that account for uncertainties and non-convex load behaviors, improving market participation accuracy.

Contribution

It proposes a novel convex quadratic classifier-based approach to approximate the feasible flexibility set of aggregators with non-convex loads, considering uncertainties and time coupling.

Findings

Effective flexibility set approximation for various load types.

Importance of considering uncertainties and time-coupling.

Validated on small and large aggregator scenarios.

Abstract

Bundling a large number of distributed energy resources through a load aggregator has been advocated as an effective means to integrate such resources into whole-sale energy markets. To ease market clearing, system operators allow aggregators to submit bidding models of simple prespecified polytopic shapes. Aggregators need to carefully design and commit to a polytope that best captures their energy flexibility along a day-ahead scheduling horizon. This work puts forth a model-informed data-based optimal flexibility design for aggregators, which deals with the time-coupled, uncertain, and non-convex models of individual loads. The proposed solution first generates efficiently a labeled dataset of (non)-disaggregatable schedules. The feasible set of the aggregator is then approximated by an ellipsoid upon training a convex quadratic classifier using the labeled dataset. The ellipsoid is subsequently inner approximated by a polytope. Using Farkas lemma, the obtained polytope is finally inner approximated by the polytopic shape dictated by the market. Numerical tests show the effectiveness of the proposed flexibility design framework for designing the feasible sets of small- and large-sized aggregators coordinating solar photovoltaics, thermostatically-controlled loads, batteries, and electric vehicles. The tests further demonstrate that it is crucial for the aggregator to consider time-coupling and uncertainties in optimal flexibility design.