Compensating balancing demand by spatial load migration – The case of geographically distributed data centers

doi:10.1016/j.enpol.2019.06.063

Energy Policy

Volume 132, September 2019, Pages 1130-1142

https://doi.org/10.1016/j.enpol.2019.06.063 Get rights and content

Highlights

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Under certain conditions, load migration allows to compensate balancing demand without conventional mechanism.
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Illustration of a decisive advantage of load migration when market areas are virtually interconnected.
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Simulation study illustrates economic feasibility of balancing demand compensation.
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Economic potential of load migration with three large data centers reaches compensation potential of approx. 7 GWh.

Abstract

The increasing share of renewables confronts existing power grids with a massive challenge, stemming from additional volatility to power grids introduced by renewable energy sources. This increases the demand for balancing mechanisms, which provide balancing power to ensure that power supply always meets with demand. However, the ability to provide cost-efficient and eco-friendly balancing power can vary significantly between locations. Fridgen et al. (2017) introduce an approach based on geographically distributed data centers, aiming at the spatial migration of balancing power demand between distant locations. Although their approach enables the migration of balancing demand to cost-efficient and/or eco-friendly balancing mechanisms, it will come up against limits if deployed on a global scale. In this paper, we extend Fridgen et al. (2017)'s approach by developing a model based on geographically distributed data centers, which not only enables the migration of balancing demand but also compensates for this migration when it is contradictory between different balancing power markets without burdening conventional balancing mechanisms. Using a simulation based on real-world data, we demonstrate the possibility to exploit the potential of compensation balancing demand offered by spatial load migration resulting in economic gains that will incentivize data center operators to apply our model.

Section snippets

Motivation

In power grids, power generation must always meet power demand (Müller and Rammerstorfer, 2008; Rammerstorfer and Wagner, 2009). Since demand fluctuates, and is not precisely known a priori (Flinkerbusch and Heuterkes, 2010), power generation has so far been regularly adjusted in order to ensure power grid stability. However, not all energy generation types are equally appropriate to adjust their output in order to contribute to grid stability. The key challenge that accompanies generation

Demand-side flexibility based on geographically distributed DCs

In general, every DSF measure corresponds to one of the two prominent DSF concepts: load shedding and load shifting (Derakhshan et al., 2016; Feuerriegel and Neumann, 2014). Load shedding involves the abatement of a scheduled power-consuming activity, for example, switching off street lights during a power shortage (Papagiannis et al., 2008). Load shifting, on the other hand, involves the postponement of a power-consuming activity, for example, interrupting the charging process of an electric

Setup

Our setup builds upon a strategy suggested by Fridgen et al. (2017) involving two geographically distributed DCs, one of which participates in the local BP market and the other of which has access to a local balancing mechanism. We extend this setup by introducing a third DC (Fig. 1).

Each of the three DCs is located in a separate market area, e.g., in a different country or even on a different continent. In line with Fridgen et al. (2017), all of the three DCs provide the same information

Evaluation of the BP migration's economic and balancing potential

To analyze the economic and balancing potential of virtually interconnecting distant BP markets, we decided to conduct a comprehensive simulation study based on real-world data.

Summary and policy implications

Our paper illustrates the economic and balancing potential of virtually interconnecting different BP markets by spatial load migration based on geographically distributed DCs. However, we do not only give an initial estimation of the economic potential, but also describe the resulting policy-related implications.

DSF can be principally differentiated between temporally or spatially utilization of flexibility Fridgen et al. (2017). This differentiation is also possible for the flexibility of DCs (

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Researchers have realized the value of DCs in solving SNCPs, such as solving congestion [25] and decreasing loss [47]. In [52,53], the spatial control method is directly separated from TLS methods, which is a typical instance of SLM that applies computation connections on space. B. Methodologies and models for DC in SLM
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View full text

Compensating balancing demand by spatial load migration – The case of geographically distributed data centers

Highlights

Abstract

Section snippets

Motivation

Demand-side flexibility based on geographically distributed DCs

Setup

Evaluation of the BP migration's economic and balancing potential

Summary and policy implications

Comput. Network.

Energy Policy

Renew. Energy

Energy Policy

Energy Policy

Energy Policy

Energy Policy

Energy Policy

Perform. Eval.

Energy Policy

Energy Policy

Renew. Sustain. Energy Rev.

Energy Policy

Energy Policy

Energy Policy

Energy Policy

Energy Policy

Energy Policy

Appl. Energy

Futur. Gener. Comput. Syst.

Energy efficient geographical load balancing via dynamic deferral of workload

Efficiency: how we do it – data centers

Decision Model for Cloud Computing under SLA Constraints

PUETM: a comprehensive examination of the metric

Fuzzy adaptive particle swarm optimization for bidding strategy in uniform price spot market

IEEE Trans. Power Syst.

Fog computing and its role in the internet of things

Fifth replacement electronic tariff

Beschreibung von Regelleistungskonzepten und Regelleistungsmarkt

ENTSO-E transparency platform

Fingrid - balancing power market

Providing utility to utilities: the value of information systems enabled flexibility in electricity consumption

J. Assoc. Inf. Syst.

The value of IS-enabled flexibility in electricity demand - a real options approach

The value of information exchange in electric vehicle charging

Integrated approach to data center power management

IEEE Trans. Comput.

Let's build a global power grid

Demand Response Opportunities and Enabling Technologies for Data Centers: Findings from Field Studies

Demand Response and Open Automated Demand Response Opportunities for Data Centers

PUE^TM: a comprehensive examination of the metric