Flattening the Duck Curve: A Case for Distributed Decision Making
Abstract
The large penetration of renewable resources has resulted in rapidly changing net loads, resulting in the characteristic "duck curve". The resulting ramping requirements of bulk system resources is an operational challenge. To address this, we propose a distributed optimization framework within which distributed resources located in the distribution grid are coordinated to provide support to the bulk system. We model the power flow of the multi-phase unbalanced distribution grid using a Current Injection (CI) approach, which leverages McCormick Envelope based convex relaxation to render a linear model. We then solve this CI-OPF with an accelerated Proximal Atomic Coordination (PAC) which employs Nesterov type acceleration, termed NST-PAC. We evaluate our distributed approach against a local approach, on a case study of San Francisco, California, using a modified IEEE-34 node network and under a high penetration of solar PV, flexible loads, and battery units. Our distributed approach reduced the ramping requirements of bulk system generators by up to 23%.
Cite
@article{arxiv.2111.06361,
title = {Flattening the Duck Curve: A Case for Distributed Decision Making},
author = {Rabab Haider and Giulio Ferro and Michela Robba and Anuradha M. Annaswamy},
journal= {arXiv preprint arXiv:2111.06361},
year = {2022}
}
Comments
5 pages, 4 figures, 1 table. This work has been accepted for presentation at the 2022 IEEE Power & Energy Society General Meeting, and will be a part of the conference proceedings.Copyright may be transferred without notice, after which this version may no longer be accessible