Dehghan, S, Nakiganda, A orcid.org/0000-0003-3017-5525 and Aristidou, P orcid.org/0000-0003-4429-0225 (2020) A Data-Driven Two-Stage Distributionally Robust Planning Tool for Sustainable Microgrids. In: 2020 IEEE Power & Energy Society General Meeting (PESGM). 2020 IEEE Power & Energy Society General Meeting (PESGM), 02-06 Aug 2020, Montreal, QC, Canada. IEEE ISBN 978-1-7281-5509-8
Abstract
This paper presents a data-driven two-stage distributionally robust planning tool for sustainable microgrids under the uncertainty of load and power generation of renewable energy sources (RES) during the planning horizon. In the proposed two-stage planning tool, the first-stage investment variables are considered as here-and-now decisions and the second-stage operation variables are considered as wait-and-see decisions. In practice, it is hard to obtain the true probability distribution of the uncertain parameters. Therefore, a Wasserstein metric-based ambiguity set is presented in this paper to characterize the uncertainty of load and power generation of RES without any presumption on their true probability distributions. In the proposed data-driven ambiguity set, the empirical distributions of historical load and power generation of RES are considered as the center of the Wasserstein ball. Since the proposed distributionally robust planning tool is intractable and it cannot be solved directly, duality theory is used to come up with a tractable mixed-integer linear (MILP) counterpart. The proposed model is tested on a 33-bus distribution network and its effectiveness is showcased under different conditions.
Metadata
Item Type: | Proceedings Paper |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | Uncertainty , Microgrids , Tools , Probability distribution , Planning , Power generation , Load modeling |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Institute of Communication & Power Networks (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 17 Sep 2021 14:21 |
Last Modified: | 18 Sep 2021 06:58 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/pesgm41954.2020.9281869 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:178192 |