Distributed energy resource exploitation through co-optimization of power system and data centers with uncertainties during demand response

This paper presents a robust bi-level co-optimization model that promotes the active participation of Internet Data Centers (IDCs) in demand response (DR) programs, thereby enhancing the flexibility of power systems. Our approach involves leveraging virtual power lines to migrate workloads among IDC...

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Main Authors: Weng, Yu, Liu, Yang, Lim, Rachel Li Ting, Nguyen, Hung D.
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2023
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Online Access:https://hdl.handle.net/10356/171064
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1710642023-10-13T15:41:00Z Distributed energy resource exploitation through co-optimization of power system and data centers with uncertainties during demand response Weng, Yu Liu, Yang Lim, Rachel Li Ting Nguyen, Hung D. School of Electrical and Electronic Engineering Engie Lab, Singapore Engineering::Electrical and electronic engineering Bilevel Optimization Demand Response This paper presents a robust bi-level co-optimization model that promotes the active participation of Internet Data Centers (IDCs) in demand response (DR) programs, thereby enhancing the flexibility of power systems. Our approach involves leveraging virtual power lines to migrate workloads among IDCs, optimizing resource allocations, and benefiting both domains. The model incorporates a Gaussian Process Regression (GPR)-constructed DR price–amount curve, which largely contributes to the simplification of the optimization problem with high accuracy and computational efficiency. It also respects the information barriers between the two domains of power systems and IDCs, and thus safeguards the privacy and flexibility of IDCs. The uncertainty in IDC operations is considered by incorporating the variance in GPR into the demand response curve. By integrating IDCs as DR resources, the framework of this research enhances the flexibility of power systems and the efficiency of cross-domain co-optimization. The model and algorithm are validated using modified IEEE test systems. Energy Market Authority (EMA) Ministry of Education (MOE) Nanyang Technological University National Research Foundation (NRF) Published version This research is supported by NTU SUG, MOE AcRF TIER 1 RG60/22, EMA & NRF EMA-EP004-EKJGC-0003, NRF DERMS for Energy Grid 2.0, and Intra-CREATE Seed Fund NRF2022-ITS010-0005. 2023-10-11T02:04:59Z 2023-10-11T02:04:59Z 2023 Journal Article Weng, Y., Liu, Y., Lim, R. L. T. & Nguyen, H. D. (2023). Distributed energy resource exploitation through co-optimization of power system and data centers with uncertainties during demand response. Sustainability, 15(14), 10995-. https://dx.doi.org/10.3390/su151410995 2071-1050 https://hdl.handle.net/10356/171064 10.3390/su151410995 2-s2.0-85166217999 14 15 10995 en RG60/22 EMA-EP004-EKJGC-0003 NRF2022- ITS010-0005 Sustainability © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering
Bilevel Optimization
Demand Response
spellingShingle Engineering::Electrical and electronic engineering
Bilevel Optimization
Demand Response
Weng, Yu
Liu, Yang
Lim, Rachel Li Ting
Nguyen, Hung D.
Distributed energy resource exploitation through co-optimization of power system and data centers with uncertainties during demand response
description This paper presents a robust bi-level co-optimization model that promotes the active participation of Internet Data Centers (IDCs) in demand response (DR) programs, thereby enhancing the flexibility of power systems. Our approach involves leveraging virtual power lines to migrate workloads among IDCs, optimizing resource allocations, and benefiting both domains. The model incorporates a Gaussian Process Regression (GPR)-constructed DR price–amount curve, which largely contributes to the simplification of the optimization problem with high accuracy and computational efficiency. It also respects the information barriers between the two domains of power systems and IDCs, and thus safeguards the privacy and flexibility of IDCs. The uncertainty in IDC operations is considered by incorporating the variance in GPR into the demand response curve. By integrating IDCs as DR resources, the framework of this research enhances the flexibility of power systems and the efficiency of cross-domain co-optimization. The model and algorithm are validated using modified IEEE test systems.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Weng, Yu
Liu, Yang
Lim, Rachel Li Ting
Nguyen, Hung D.
format Article
author Weng, Yu
Liu, Yang
Lim, Rachel Li Ting
Nguyen, Hung D.
author_sort Weng, Yu
title Distributed energy resource exploitation through co-optimization of power system and data centers with uncertainties during demand response
title_short Distributed energy resource exploitation through co-optimization of power system and data centers with uncertainties during demand response
title_full Distributed energy resource exploitation through co-optimization of power system and data centers with uncertainties during demand response
title_fullStr Distributed energy resource exploitation through co-optimization of power system and data centers with uncertainties during demand response
title_full_unstemmed Distributed energy resource exploitation through co-optimization of power system and data centers with uncertainties during demand response
title_sort distributed energy resource exploitation through co-optimization of power system and data centers with uncertainties during demand response
publishDate 2023
url https://hdl.handle.net/10356/171064
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