dc4cities: following the patterns of renewable power in a smart city

SONJA KLINGERT – UNIVERSITY OF MANNHEIMDataCloud Europe 2015

DC4Cities: Following the Patterns of Renewable Power in

a Smart City

SONJA KL INGERT

D C 4 C I T I E S g r o u p

Follow us! @ D C 4 C I T I E S

SONJA KLINGERT – UNIVERSITY OF MANNHEIMDataCloud Europe 2015

General Approach

Data centres in the cityLack of locally produced renewable energy due to

space limitations. -> minimize energy consumption and adhere to constraints based on renewable energy supply

SONJA KLINGERT – UNIVERSITY OF MANNHEIM

High-Level Architecture

DataCloud Europe 2015


Power Planner Component

Renewables(local source)

Pow

er

Scaled powerproportional to grid ren%

Final power plan, including Renewables

Pow

er

Pow

er

+


- =


Energy Adaptation within a DC

Multi-level API for IaaS, PaaS and SaaS



Results – HP and Trento

Batch jobs: Producing 4320 reports per dayPercentage of renewable energy in the Italian grid

varies between 29,21% and 49,18% (avg. 37,16)Data from HP experiment

Uniform workload distribution over 24 hours Workload concentrated at grid max RenPerc

37,16% 42,20%



Results –HP and Trento (cont.)

When adding 8 local solar panels (max 250Wh) to the previous setting, the RenPercent rises to 79,41%

Local Solar Energy Production



DC4Cities Business Issues

Benefit !> CostBenefit

Energy budget currently no incentives Marketing/CSR/CRM doubtful

Cost: mostly flexibility, e.g business model



Flexibility in a DC


SLAGreenSLA

RenEnergy Contracts/Incentives

Technical flexibility, e.g apps., infrastr.

Customer flexibility: customization

Political framework and boundaries


Starting Point: Smart Cities


London 7.074Madrid 3.265Paris 2.212Barcelona 1.620Cologne 1.007Amsterdam 780Helsinki 589Frankfurt 680Copenhagen

542

Brussels 156Smart Cities’ Data Centres: 68 Smart Cities with 43

Mio peopleAdd: Weather/Climate Conditions


Step 3: Scenario Set-up


Conclusions

Increasing share of renewables by following patterns of renewable supply is technically feasible, but highly dependent on power infrastructure and flexibilities of applications

Economic incentives increase scopeDC4Cities can be used to tune the most

efficient infrastructure for on-site generationTrials: results are best when variability of

renewables in the grid is high – because then there are more opportunities to adapt

Business Perspectives: South Europe SC, BCN?


QUESTIONS?

Thank you!

K L I N G E R T @ I N F O R M AT I K . U N I -M A N N H E I M . D E

W W W. D C 4 C I T I E S . E U

D C 4 C I T I E S g r o u p

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The DC4Cities Architecture

1. DC4cities process controller retrieves the next 24 hours energy forecasts for each EP of the DC through the ERDS handler

2. The Max/Ideal power plan is computed3. The power plan is split into different plans, one for each service hosted by the DC

4. Multiple splitting policies can be configured to better tailor the system to the DC business needs

5. The controller will request EASC to create specific power budgets for the next 24 hours for each service

6. The Option plan collector will receive a set of possible alternatives by each EASC

7. All Option plans will be consolidated and globally optimized to achieve the best usage of renewable energy source

8. If a good solution is found, the EASCs are informed which option plan to enact. Else, an escalation process is triggered [8x]

9. EASC will use automation tools to control the SW/HW resources of the service in line with the received plan (Working Mode).

10. Finally the controller will share the DC power plan with the energy provider, to enable some form of demand/response cooperation


dc4cities: following the patterns of renewable power in a smart city

Technology

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