Distributed Economic Dispatch in Power Networks Harnessing Data Center Flexibility

Athanasios Tsiligkaridis, Panagiotis Andrianesis, Ayse Coskun, Michael Caramanis and Ioannis Ch. Paschalidis
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Athanasios Tsiligkaridis: College of Engineering, Boston University - BU - Boston University [Boston]
Panagiotis Andrianesis: PERSEE - Centre Procédés, Énergies Renouvelables, Systèmes Énergétiques - Mines Paris - PSL (École nationale supérieure des mines de Paris) - PSL - Université Paris Sciences et Lettres
Ayse Coskun: College of Engineering, Boston University - BU - Boston University [Boston]
Michael Caramanis: College of Engineering, Boston University - BU - Boston University [Boston]
Ioannis Ch. Paschalidis: College of Engineering, Boston University - BU - Boston University [Boston]

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Abstract: In this work, we focus on the interaction of Data Centers (DCs) with electric power networks in an economic dispatch problem, harnessing the ability of DCs to serve as flexible loads that can alter their power consumption to alleviate network congestion. Since DCs can transfer workload between each other, controlling their output via some demand response mechanism that respects power generation and network constraints, while also accounting for DC Quality of Service (QoS) can achieve system-wide benefits. From the DC perspective, we aim to explore the benefits of their incorporation in an economic dispatch problem, and demonstrate that they can achieve significant cost savings for the entire system (data and power networks), enabling the IT sector to contribute to societal sustainability efforts. Our main contributions are as follows. First, we leverage results from queuing theory to model DCs and form QoS-based cost functions — signifying how well a DC can carry out its workload given an amount of active servers, for which we provide convexity guarantees under certain conditions. Second, we integrate DCs in a centralized economic dispatch problem that determines, apart from power generation, DC workload shifting and server utilization, while respecting power network constraints. Third, we provide a tractable decentralized formulation of the economic dispatch problem employing Lagrangian decomposition and a primal-dual al- gorithm, which can cater for both the power network constraints, and, most importantly, the DC workload shifting, in a distributed manner that scales for the "coupled" data and power networks. Fourth, we present experimental results on a standard power network that provide useful insights on the system-wide benefits from harnessing DC flexibility in the economic dis- patch problem, emphasizing on the trade-offs between the DC locations, their efficiencies, and QoS costs.

Date: 2024-11-19
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Published in PGMODays 2024, Nov 2024, Palaiseau, France.

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04883308

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