1.
Page 1SONJA KLINGERT – UNIVERSITY OF MANNHEIMDataCloud Europe 2015
DC4Cities: Following the
Patterns of Renewable Power in
a Smart City
S O N J A K L I N G E R T
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

2.
Page 2SONJA KLINGERT – UNIVERSITY OF MANNHEIMDataCloud Europe 2015
General Approach
 Data centres in the city
 Lack of locally produced renewable energy due to
space limitations.
-> minimize energy consumption and adhere to
constraints based on renewable energy supply

3.
Page 3SONJA KLINGERT – UNIVERSITY OF MANNHEIM
High-Level Architecture
DataCloud Europe 2015

4.
Page 4SONJA KLINGERT – UNIVERSITY OF MANNHEIM
Power Planner Component
Renewables
(local source)
Power
Scaled power
proportional to grid ren%
Final power plan,
including Renewables
Power
Power
+
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- =

5.
Page 5SONJA KLINGERT – UNIVERSITY OF MANNHEIM
Energy Adaptation within a DC
 Multi-level API for IaaS, PaaS and SaaS
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6.
Page 6SONJA KLINGERT – UNIVERSITY OF MANNHEIM
Results – HP and Trento
 Batch jobs: Producing 4320 reports per day
 Percentage 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%
DataCloud Europe 2015

7.
Page 7SONJA KLINGERT – UNIVERSITY OF MANNHEIM
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
DataCloud Europe 2015

8.
Page 8SONJA KLINGERT – UNIVERSITY OF MANNHEIM
DC4Cities Business Issues
 Benefit !> Cost
 Benefit
 Energy budget  currently no incentives
 Marketing/CSR/CRM  doubtful
 Cost: mostly flexibility, e.g business model
DataCloud Europe 2015

9.
Page 9SONJA KLINGERT – UNIVERSITY OF MANNHEIM
Flexibility in a DC
DataCloud Europe 2015
SLA
GreenSLA
RenEnergy Contracts/Incentives
Technical flexibility,
e.g apps., infrastr.
Customer flexibility:
customization
Political framework
and boundaries

10.
Page 10SONJA KLINGERT – UNIVERSITY OF MANNHEIM
Starting Point: Smart Cities
DataCloud Europe 2015
London 7.074
Madrid 3.265
Paris 2.212
Barcelona 1.620
Cologne 1.007
Amsterdam 780
Helsinki 589
Frankfurt 680
Copenhagen 542
Brussels 156
 Smart Cities’ Data Centres: 68 Smart Cities with
43 Mio people
 Add: Weather/Climate Conditions

11.
Page 11SONJA KLINGERT – UNIVERSITY OF MANNHEIM
Step 3: Scenario Set-up

12.
Page 12SONJA KLINGERT – UNIVERSITY OF MANNHEIM
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 scope
 DC4Cities can be used to tune the most efficient
infrastructure for on-site generation
 Trials: 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?
DataCloud Europe 2015

13.
Page 13
Q U E S T I O N S ?
Thank you!
K L I N G E R T @ I N F O R M A T 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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Contact us!

14.
Page 14SONJA KLINGERT – UNIVERSITY OF MANNHEIM
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
DataCloud Europe 2015