Data-Driven Modeling of Aggregate Flexibility Under Uncertain and Non-Convex Device Models

Bundling a large number of distributed energy resources through a load aggregator has been advocated as an effective means to integrate such resources into wholesale energy markets. To ease market clearing, system operators allow aggregators to submit bidding models of simple prespecified polytopic...

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Bibliographic Details
Published in:IEEE transactions on smart grid 2022-11, Vol.13 (6), p.4572-4582
Main Authors: Taheri, Sina, Kekatos, Vassilis, Veeramachaneni, Sriharsha, Zhang, Baosen
Format: Article
Language:English
Subjects:
aggregator
Approximation
Batteries
containment of polytopes
convex quadratic classifier
Datasets
day-ahead markets
Distributed generation
Distributed power generation
Electric vehicles
Ellipsoids
Energy industry
Energy sources
Feasible set
Flexibility
load disaggregation
Load modeling
optimal flexibility design
Photovoltaic cells
Polytopes
Schedules
Scheduling
Task analysis
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Summary:Bundling a large number of distributed energy resources through a load aggregator has been advocated as an effective means to integrate such resources into wholesale 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 (in)-feasible aggregation 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.
ISSN:1949-3053
1949-3061
DOI:10.1109/TSG.2022.3185532