How Can Datacenters Join the Smart Grid to Address the Climate Crisis? Using simulation to explore power and cost effects of direct participation in the energy market

TL;DR

This paper explores how datacenters can actively participate in energy markets to reduce costs and support the power grid, proposing a new market participation scheme and a machine learning-based DVFS scheduling algorithm.

Contribution

It introduces a novel short-term market participation scheme for datacenters and a machine learning-enhanced DVFS scheduler to optimize energy use and provide demand response.

Findings

Datacenters can significantly reduce energy costs through direct market participation.

The proposed scheduler effectively decreases energy consumption during market operations.

Simulations show strong financial and grid support benefits for datacenters.

Abstract

Amidst the climate crisis, the massive introduction of renewable energy sources has brought tremendous challenges to both the power grid and its surrounding markets. As datacenters have become ever-larger and more powerful, they play an increasingly significant role in the energy arena. With their unique characteristics, datacenters have been proved to be well-suited for regulating the power grid yet currently provide little, if any, such active response. This problem is due to issues such as unsuitability of the market design, high complexity of the currently proposed solutions, as well as the potential risks thereof. This work aims to provide individual datacenters with insights on the feasibility and profitability of directly participating in the energy market. By modelling the power system of datacenters, and by conducting simulations on real-world datacenter traces, we demonstrate the substantial financial incentive for individual datacenters to directly participate in both the day-ahead and the balancing markets. In turn, we suggest a new short-term, direct scheme of market participation for individual datacenters in place of the current long-term, inactive participation. Furthermore, we develop a novel proactive DVFS scheduling algorithm that can both reduce energy consumption and save energy costs during the market participation of datacenters. Also, in developing this scheduler, we propose an innovative combination of machine learning methods and the DVFS technology that can provide the power grid with indirect demand response (DR). Our experimental results strongly support that individual datacenters can and should directly participate in the energy market both to save their energy costs and to curb their energy consumption, whilst providing the power grid with indirect DR.