Carbon footprint reduction of a cloud computing service using a - PowerPoint PPT Presentation

Carbon footprint reduction of a cloud computing service using a predictive dynamic LCA model Elsa Maurice, Thomas Dandres, Reza Farrahi Moghaddam, Kim Nguyen,Yves Lemieux, Mohamed Cherriet and Réjean Samson SAM8 Seminar, May 20-21, 2014 elsa.maurice@gmail.com

PLAN INTRODUCTION OBJECTIVES METHOD RESULTS DISCUSSION & CONCLUSION

INTRODUCTION Electricity demand around the world 6 th largest consumer 1 2 3 4 5 Source: Greenpeace, April 2014

INTRODUCTION Canadian project to reduce ICT carbon footprint Green Sustainable Telco Cloud  Server nodes in Alberta, Ontario and Quebec How to measure  in real time the Time dependent electricity consumption environmental impacts of such  Dynamic server-load cloud computing migrations depending on system? the emissions attributed to each server node in real time

INTRODUCTION Data center power Electricity generation demand variability variability Electricity= E(t) depends on demand (t) and supply (t) e(t) Instantaneous power demand related to CPU/Memory use and α base power demand of (2) Annual load curve servers (when CPU/Memory is not used) for an electric utility: Dynamic generation (1) Energy consumption depends on server workload: Dynamic demand Source: Zuker, R. C., et al. (1984). Blue Gold. Source: Gebert, S. (2012)  Servers optimization and emission assessment need a precise monitoring of electricity grid mix variations at each hour of the day

INTRODUCTION Regionalization of the electricity flows (2012-2013) Québec Alberta Nuclear Hydro Natural gas Wind Coal Ontario  Electricity carbon footprint has to consider every energy sources  Approach by electricity grid mixes

OBJECTIVES Main Objective • Improve the modeling of electricity flow in life cycle assessment by taking into account temporal variations ◦ Compute dynamic electric grid mixes from historic data Secondary Objectives • Assess real time GHG emissions of an end-user of a cloud computing service • Develop a future oriented dynamic A-LCA method to improve data center management

METHOD – LCA system boundaries

METHOD – Electricity modeling in attributional LCA Conventional Attributional LCA approach A-LCA (static) Demand Data collection: Alberta  Regionalized grid mix production Québec Electricity  Annual electricity generation (average) Ontario Limits : temporal aggregation in LCA prevents the modeling of accurate emissions of processes that consumes electricity in an irregularly manner

METHOD – Electricity modeling in dynamic LCA Dynamic historical Attributional LCA approach Dynamic LCA = time dependent function • Data collection: Demand = f(t) Alberta = A(t)  Regionalized grid mix ? production = g(t)  Historic data of Québec = Q(t) Electricity electricity generation ? (hour by hour over years) Ontario = O(t) ?  Real time data of electricity generation • Limits: Difficult to collect data, hard to implement in a LCA model

METHOD – Data collection Sources of data  Real-time and historic data from Ontario Source of data: IESO Power to Ontario Demand (http://reports.ieso.ca/public/GenOutputCapability/)  Real-time and historic data from Alberta Source of data: AESO Alberta Electric System Operator (http://ets.aeso.ca/ets_web/ip/Market/Reports/CSDReportServlet/)

METHOD – Data processing (Ontario and Alberta) Grid-mix Day 1 Carbon footprint Energy output Per day Emission kg C02 Historic Hours Day 2 data Energy output Hours Hours (1) Monthly average: (2) Seasonal average Each daily profile is combined Each daily profile is combined over one month over each season Month = function(hours) Season = function (hours)

RESULTS – Ontario grid mix and GHG emission factor Electric grid mix variations percentage electricty Electric grid mix in Ontario at 3pm (november-may) 100% OTHER Total per source (%) WIND Total 80% HYDRO Total 60% GAS Total 40% COAL Total 20% NUCLEAR Total 0% Nov-2012 Dec-2012 Jan-2013 Feb-2013 Mar-2013 Apr-2013 Day Daily greenhouse gas emission in Ontario (g CO 2 -eq. /kWh) 350 Max: 298 g CO 2 -eq./kWh 300 Greenhouse gas emission 250 factor gCO2/kWh 200 150 100 Min: 44 g CO 2 -eq./kWh 50 0 Nov-2012 Dec-2012 Jan-2013 Feb-2013 Mar-2013 Apr-2013

METHOD – Data collection Dynamic A-LCA approach: data collection  Real-time and historic data from Ontario Source of data: IES O Power to Ontario Demand (http://reports.ieso.ca/public/GenOutputCapability/)  Real-time and historic data from Alberta Source of data: AESO Alberta Electric System Operator (http://ets.aeso.ca/ets_web/ip/Market/Reports/CSDReportServlet/) X No real-time or precise historic data from Quebec Monthly data of Quebec electricity generated from Statistic Canada Extrapolation of Quebec daily electricity generation from neighbours consumption and import-export at interconnections

METHOD – Quebec electricity modeling Dynamic A-LCA future oriented model for Quebec Quebec electricity mix (t) = Production (t) + Imports (t) - Exports (t) Quebec imports/exports modeling:  Historic data of import/export at interconnections  Marginal electricity sources identified by Ben Amor in its thesis (2006-2008 study) Marginal sources of electricity by region New-Brunswick Ontario Quebec (50% Coal, (Coal) (Hydro) 50% natural gas) New-York New-England (50% Coal, (Natural gas) 50% natural gas)

METHOD – Quebec electricity modeling Dynamic A-LCA future oriented model for Quebec Dynamic A-LCA model Study of historical electrical trends of Quebec neighbors Calculation of a multi parameters function of net imports: Net import (day, hour) = A × Demand + B × Temperature + C × Price + D × Time + E of region i • A,B,C, D and E = Constants • i = Ontario, New-York, New England or New Brunswick • Net import day j of region i, Net import = Import (province i from quebec) – exportation (province i to quebec) • Demand day j-1 or predicted • Temperature day j • Price day j-1 or predicted Dynamic A-LCA predictive model

RESULTS Dynamic A-LCA future oriented model for Quebec System of equations  One equation per month  One equation per interconnection (Ontario, New-Brunswick, New-England and New-York)  Good deviation factor: 0,84 < r 2 < 0,99 Example : December 2013, interconnection: Quebec  New-England Total Net Import QC (MWh) =- 0,0790 * Day Ahead_demand (MWh) + 5,6229 * Temperature ( ° C) + 4,6307 * Day Ahead_Local Marginal Price ($/MWh) - 0,3962 * Hours - 739,8551 Total Net Import QC x Marginal technology = Environmental impact

RESULTS – Quebec grid mix Implementing imports in dynamic electricity grid mix Quebec local electricity generation (2013) Quebec local electricity generation with net imports (December 2013) Wind Natural gas Nuclear Hydro 100,00% 100% 99,75% 98% 99,50% 96% 99,25% 94% 99,00% 92% 98,75% 90% 98,50% 88% janv-13 mars-13 Apr-2013 July-2013 September-13 October-13 Feb-2013 May-2013 June-2013 August-2013 November 2013 December-2013 1 3 5 7 9 11 13 15 17 19 21 23 Hours Months Import-export modeling have inserted a significant part of Coal in the Quebec electricity mix

GHG emissions of electricity consumed in Quebec

DISCUSSION Smart network management • Optimization of punctual or daily activities such as maintenance, instant messaging… to reduce environmental impacts • Optimization of server-load migrations across the Green Sustainable Telco Cloud … to reduce environmental impacts Combine Dynamic LCA with consequential LCA • to take into account marginal sources of electricity of domestic electricity generation Keep on collecting data to improve model precision • Update marginal technologies according to 2014 data Application • Carbon tax • Transparency • Minimization of the emissions directly by the end-user

CONCLUSION A-LCA Dynamic approach • The difference between the annual average electric grid mix and the real- time electric grid mix may be significant • A better understanding of electricity flows at different scales of time: day, season, year • Improve the integration in A-LCA of electricity generation temporal variations • Improve inventory in A-LCA. Environmental impacts in Dynamic LCA are computed with a higher accuracy • Future oriented models can help to make decisions and to anticipate the electricity trades between Canada and United States

QUESTIONS Thank you for your attention!!

AKNOWLEDGEMENTS