Distributed optimization in energy communities: a focus on flexibility provision

Abstract

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The emergence of energy communities (ECs) as new stakeholders in modern electrical grids presents an opportunity to address the challenges associated with integrating distributed energy resources, such as increased uncertainty and variability in generation, transmission grid saturation, or diminished power quality, among others. The ability of an EC to provide flexibility is therefore paramount. However, data privacy and investment costs are the main drawbacks to achieving optimal operation. This paper proposes a distributed optimization framework designed for the economic dispatch (ED) of ECs, employing the auxiliary problem principle (APP) decomposition technique. The primary focus is to study the effects of flexibility provision in real-time control while addressing data privacy concerns and minimizing communication infrastructure investments. The proposed algorithm is tested in a case study, providing insights into the selection of the control parameters of the APP framework to improve the convergence properties.

Postprint (author's final draft)