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
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