Smart City Cluster Collaboration, Task 4. Measurement and Verification Methodology for Energy, Environmental and Economic Efficiency Metrics.

1
Smart City Cluster Collaboration
Task 4
Data Centre Integration
Energy, Environmental, and Economic Efficiency Metrics:
Measurement and Verification Methodology

2
Revision History
VERSION DATE PARTNERS DESCRIPTION/COMMENTS
4.1.1 2015 – 04 – 20 Jacinta Townley
(GENiC /UTRCI)
Complied the work completed for Task 4.1
4.1.2 2015 – 04 – 22 Jaume Salom
(RenewIT / IREC)
Comments
4.1.3
4.1.4
4.1.5
2015 – 04 – 28
2015 – 06 – 12
2015 – 06 – 12
Vasiliki Georgiadou (GEYSER
/ Green IT Amsterdam)
Jacinta Townley
(GENiC/UTRCI)
Jacinta Townley
(GENiC/UTRCI)
Review comments
Updated sections based on comments, made
some additional text change and added
standardisation input.
Updated ERE with footnote and extended intro
section.
4.1.6 2015 – 06 - 25 Silvia Sanjoaquín Vives
(DC4Cities/ GNF)
Last review
4.2.1 2015 – 04 – 24 Daniela Isidori
(RenewIT/Loccioni)
Complied the work completed for Task 4.2
4.2.2 2015 – 05 – 18 Gonzalo Díaz Vélez / Silvia
Sanjoaquín Vives (DC4Cities
/ GNF)
Document review
4.2.3 2015 – 05 – 18 Daniela Isidori
(RenewIT/Loccioni)
Document fixing after review
4.2.4 2015 – 06 – 01 Andrea Quintiliani
(DC4Cities / ENEA)
Example for EES and few minor corrections in the
EES chapter
4.2.5 2015 – 06 – 03 Daniela Isidori
(RenewIT/Loccioni)
Examples for all metrics except for GUF
4.2.6 2015 – 06 – 04 Daniela Isidori
(RenewIT/Loccioni
Document fixing after review (Figures, Tables,
Sections and Equations cross references)
4.2.7 2015 – 06 -04 Vasiliki Georgiadou (GEYSER
/ Green IT Amsterdam)
Review
4.2.8 2015 – 06 -18 Jaume Salom (RenewIT/Irec) Review
4.2.9 2015 – 06 -19 Daniela Isidori
(RenewIT/Loccioni)
Last Review
4.2.10 2015-06-25 Silvia Sanjoaquín Vives
(DC4Cities/ GNF)
Last review
4.1 2015 – 06 – 29 Alexis Aravanis
(DOLFIN/Synelixis)
Task 4.1 and 4.2 consolidation
4.2 2015 -07 - 06 Silvia Sanjoaquín Vives
(DC4Cities/GNF)
Last review

3
Metric M&V Synopsis
Metric Full Name Adaptability Power Curve
Metric Short Name APC
Metric Category DC Flexibility: Energy Shifting
Metric Description It provides an evaluation of the capability of a DC to adapt to a pre-defined DC energy
consumption curve.
Metric Category Leader DOLFIN/Synelixis
Developing partners Artemis Voulkidis, Alexis Aravanis - DOLFIN/Synelixis
Andrea Quintiliani, Marta Chinnici - DC4Cities/ENEA
Vasiliki Georgiadou – GEYSER/Green IT Amsterdam
Massimiliano Manca – RenewIT/Loccioni
Tomas Fernandez Buckley – Acciona/GENiC
Reviewer partners Milagros Rey Porto, Silvia Sanjoaquín Vives, Gonzalo Díaz Vélez – DC4Cities/GNF;
Andrea Quintiliani, - DC4Cities/ENEA
Metric Full Name Adaptability Power Curve at Renewable Energies
Metric Short Name
Metric Category Data Centre Flexibility: Energy Shifting
Metric Description
It provides an evaluation of the capability of a data centre to adapt to the production
curve of the renewable energy sources available to the data centre in hand.
Developing partners
Artemis Voulkidis, Alexis Aravanis - DOLFIN/Synelixis
Andrea Quintiliani, Marta Chinnici - DC4Cities/ENEA
Reviewing partners
Milagros Rey Porto/Silvia Sanjoaquín Vives, Gonzalo Díaz Vélez – DC4Cities/GNF,
Andrea Quintiliani, - DC4Cities/ENEA
Metric Full Name Data Centre Adapt
Metric Short Name DCA
Metric Category DC Flexibility: Energy Shifting
Metric Description
It provides an evaluation of the capability of a DC to change its energy consumption
behaviour, compared to its respective behaviour before the application of a certain set
of optimisation actions

4
Developing partners
Artemis Voulkidis, Aravanis Alexis – DOLFIN/Synelixis
Andrea Quintiliani, Marta Chinnici - DC4CITIES/ENEA
Reviewing partners
Milagros Rey Porto, Silvia Sanjoaquín Vives - GNF/DC4Cities, Andrea Quintiliani, -
ENEA/DC4Cities
Metric Full Name Primary Energy Savings
Metric Short Name PE Savings
Metric Category PE Savings and CO2 avoided emissions
Metric Description
The percentage of savings in terms of primary energy consumed by a data centre, once
improvements have taken place with regard to its energetic, economic, or
environmental management
Metric Category Leader Vasiliki Georgiadou (GEYSER / Green IT Amsterdam)
Developing partners
Andrea Quintiliani, Marta Chinnici (DC4Cities / ENEA), Philip Inglesant (RenewIT / 451
Research), Davide Nardi Cesarini (RenewIT / Loccioni)
Reviewing partners
Milagros Rey / Silvia Sanjoaquín Vives / Gonzalo Díaz Vélez (DC4Cities / GNF); Philip
Inglesant (RenewIT/451 Research)
Metric Full Name CO2 Avoided Emissions
Metric Short Name CO2Savings
Metric Category PE Savings and CO2 avoided emissions
Metric Description
The percentage of savings in terms of CO2 emissions generated by a data centre, once
improvements have taken place with regard to its energetic, economic, or
environmental management
Developing partners
Andrea Quintiliani, Marta Chinnici (DC4Cities / ENEA), Philip Inglesant (RenewIT / 451
Research), Davide Nardi Cesarini (RenewIT / Loccioni)
Reviewing partners Milagros Rey Porto, Silvia Sanjoaquín Vives, Gonzalo Díaz Vélez (DC4Cities / GNF)
Metric Full Name Energy Expenses
Metric Short Name EES
Metric Category Economic savings in energy expenses
Metric Description
A measure of how much the energy related expenses have changed in comparison to a
baseline scenario, after having performed actions to upgrade the energetic, economic
or environmental behaviour of a data centre
Metric Category Leader Andrea Quintiliani, Marta Chinnici (DC4Cities / ENEA)

5
Developing partners Vasiliki Georgiadou (GEYSER / Green IT Amsterdam)
Reviewing partners
Milagros Rey Porto, Silvia Sanjoaquín Vives, Gonzalo Díaz Vélez (DC4Cities / GNF),
Anthony Schoofs (GEYSER / Wattics)
Metric Full Name Grid Utilization Factor
Metric Short Name GUF
Metric Category Renewables integration: Energy produced locally and Renewable usage
Metric Description
Percentage of time that the local generation does not cover the building demand, and
thus how often energy must be supplied by the grid
Metric Category Leader Davide Nardi Cesarini (RenewIT/Loccioni), Jaume Salom (RenewIT/IREC)
Developing partners Artemis Voulkidis (Dolfin/Synelixis)
Reviewing partners Gonzalo Díaz Vélez / Silvia Sanjoaquín Vives, Gonzalo Díaz Vélez (DC4Cities / GNF)
Metric Full Name Energy Reuse Effectiveness
Metric Short Name ERE
Metric Category Energy Recovered: Heat Recovered
Metric Description Measures the benefit of reusing any recovered energy from the data centre
Developing partners
Artemis Voulkidis, Alexis Aravanis (DOLFIN / Synelixis), Piotr Sobonski, Susan Rea,
Christopher Burge (GENiC / CIT), Philip Inglesant (RenewIT / 451 Research)
Reviewing partners
Fabrice Roudet ( GreenDataNet / Eaton), Milagros Rey Porto, Silvia Sanjoaquín Vives,
Eduard Naranjo Cardoso (DC4Cities / GNF), Paul Hughes (GEYSER / ABB), Marco Cupelli
(GEYSER / RWTH)

6
Contents
1 Introduction ..............................................................................................................................................................8
2 Task 4.1 - Methodologies for existing Metrics..........................................................................................................9
2.1 Power Usage Effectiveness (PUE) .....................................................................................................................9
2.2 Renewable Energies Factor (REF) ...................................................................................................................11
2.3 IT Equipment Energy Efficiency (ITEE) ............................................................................................................12
2.4 Cooling Effectiveness Rate (CER) ....................................................................................................................12
2.5 Energy Reuse Effectiveness (ERE) ...................................................................................................................14
2.5.1 M & V Plan Scope and Metric Overview.................................................................................................14
2.5.2 Measurements........................................................................................................................................15
3 Task 4.2 – Methodologies for new Metrics.............................................................................................................24
3.1 Adaptability Power Curve ...............................................................................................................................24
3.1.2 Measurements........................................................................................................................................25
3.1.3 Example...................................................................................................................................................28
3.2 Adaptability Power Curve at Renewable Energies..........................................................................................29
3.2.2 Measurements........................................................................................................................................29
3.2.3 Example...................................................................................................................................................32
3.3 Data Centre Adapt ..........................................................................................................................................33
3.3.2 Measurements........................................................................................................................................34
3.3.3 Example...................................................................................................................................................37
3.4 Primary Energy Savings...................................................................................................................................38
3.4.2 Measurements........................................................................................................................................39
3.4.3 Example...................................................................................................................................................44
3.5 CO2 Avoided Emissions....................................................................................................................................45
3.5.2 Measurements........................................................................................................................................47
3.5.3 Example...................................................................................................................................................48
3.6 Economic Saving in Energy Expenses (EES).....................................................................................................49
3.6.2 Measurements........................................................................................................................................52

7
3.6.3 Example...................................................................................................................................................54
3.7 Grid Utilization Factor.....................................................................................................................................55
3.7.2 Measurements........................................................................................................................................56
3.8 Analysis & Verification ....................................................................................................................................60
3.8.1 Error Filtering ..........................................................................................................................................60
3.8.2 Statistical Analysis of Baseline models....................................................................................................61
3.8.3 Uncertainty analysis of the results achieved ..........................................................................................61
3.9 Reporting.........................................................................................................................................................64
3.9.1 Reporting format.....................................................................................................................................64
3.9.2 Reporting frequency ...............................................................................................................................64
4 References ..............................................................................................................................................................65

8
1 Introduction
The standardization activities performed so far in the context of the Smart City Cluster collaboration have led to the
identification of appropriate methodologies and procedures for the calculation of new and existing metrics or Key
Performance Indicators (KPIs). Thus, allowing the standardization of procedures to the extent possible and the
determination of common baselines for the efficient comparison of Cluster projects and the aggregation and
comparison of common variables and metrics, conflating seamlessly input from all Cluster constituent projects.
The projects included in the cluster, as already detailed in section 1 of Task 3 are the following: All4Green,
CoolEmAll, GreenDataNet, RenewIT, GENiC, GEYSER, Dolfin and DC4Cities. Moreover, since April 2015 a new project
has joined the cluster, EURECA. Every one of the above projects focuses on different goals and objectives,
quantifying the performance of the involved systems by measuring different events. Hence, the comparison of the
obtained measurements requires the definition of common variables and metrics to compare results in the same
way.
In this course, Task 3 proposed new metrics based on the existing ones improving their performance. In particular,
Task 3.1 identified existing metrics that could be employed in the context of Cluster, whereas Task 3.2 built upon the
current metrics to define new for measuring concepts as e.g. the exploitation of RES and the flexibility of DCs to
adjust their energy consumption and Task 3.3 defined new metrics for measuring the performance of DCs.
However, in the direction of defining common methodologies for the measurement of common KPIs toward a
collective standard development, Task 4 focused on the definition of common measuring and verification
methodologies. Specifically, Task 4.1 presented measuring and verification methodologies for existing metrics which
are almost finalized by standardization bodies (e.g. ISO/IEC JTC 1/SC 39), using the quasi-finalized metrics as
common basis for comparison. In addition, Task 4.2 presented measuring and verification methodologies for new
metrics defined in Cluster activity Task 3.2.
The measuring and verification methodologies defined in Task 4 are compliant with the International Performance
Measurement and Verification Protocol (IPMVP). The IPMVP is a protocol developed by a consortium of
international organizations, defining standards for energy efficiency projects. Capitalizing on the success of IPMVP,
Task 4 determines measuring and verification methodologies fully in line with the successful and pervasive
methodologies of IPMVP, giving rise to a coherent Cluster protocol, allowing for the efficient comparison of Cluster
projects and the exploitation of registered measurements toward amalgamation of feedback and the emergence of a
holistic approach allowing the drawing of common Cluster conclusions.
However, a fully developed strategy on how to deal with the data gathered, stored, and analysed for the purpose of
computing, reporting, and potentially auditing the related metrics is out of scope of this cluster's goals and activities.
As such, each case would need to be handled on an ad-hoc basis, depending also on the data management systems
already in place. After the validation phase completes - see also Task 6 - the basic outline and guidelines of such a
plan will be designed and presented.

9
2 Task 4.1 - Methodologies for existing Metrics
As already mentioned above, the purpose of Task 4.1 was to take a subset of the metrics already selected by the
Smart Cities DC Cluster during Task 3 and propose measurement methodologies. Specifically, metrics defined outside
of the cluster were selected for Task 4.1. Hence, the work for this task involved considering methodologies already in
existence for these metrics, selecting those deemed most suitable for use by the cluster and, where appropriate,
extending known methodologies.
For the following Task 4.1 metrics, PUE, REF, ITEE, CER and ERE, the first three avail of existing work provided by
ISO/IEC JTC 1/SC 391
, with the methodologies for the other two being discussed below.
2.1 Power Usage Effectiveness (PUE)
ISO/IEC JTC 1/SC 39 are close to finalizing the PUE metric (ISO/IEC JTC 1/SC 39 PUE, 2015). After careful
consideration, this has been selected as the Cluster methodology for measuring PUE. However, some considerations
and comments to the draft documents have been addressed by the Cluster using ISO/IEC templates through the
French Committee. It should be noted that this feedback was provided outside the normal request process and is as
follows:
Member Body Location
Paragraph/Figure/
Table/Note
Comment Type Justification
Proposed
Change
FRO 52 Introduction ED Sentence not clear cancel ‘’it is’’ and to
replace by ‘’, there are’’
IREC Line 220 Section 5.1 ED EDC is already defined in
section 3.1.8. The use
of “Total facility
energy” also introduces
confusion
Change to “Total data
centre energy is
defined in section
3.1.8”
IREC Line 225 Section 5.1 ED The IT equipment
energy is already
defined in section 3.1.2
Delete the final
sentence in the
paragraph about IT
energy consumption
IREC Line 226 Section 5.1 ED Refer to Annex B in
case that different
energy carriers are
energy sources to data
centre. No reference to
Annex B in the main
part of the document
Add “In case that
various types of source
energy or on-site
generation systems are
serving the data centre,
refer to Annex B for
PUE calculation”
IREC Section 6.2.2 TE Section only refers to
Meter and
Measurement
requirements in case of
electricity. If other
energy carriers are
used (gas, chilled
water, etc..) other kind
of metering systems
would be required.
IREC Line 556 Annex B. Section B.1 TE The sentence “Since
PUE is not a metric to
identify the efficiency
of how electricity is
Change to “Since PUE is
not a metric to identify
the efficiency of how
1
http://www.iso.org/iso/home/standards_development/list_of_iso_technical_committees/iso_technical_committee.htm?com
mid=654019

10
brought to the data
centre, it is a metric to
identify how efficient
the electricity is used”
is a clear contradiction
with other ways of
defining PUE in the
document, as for
example, section 3.1.4
or section 5.1 where
clearly states that total
data centre energy
considers the total
energy needed for the
data centre facility
energy is brought to
the data centre, it is a
metric to identify how
efficient energy is used
within the data centre
boundaries”
IREC Annex B. Section B.1 TE A figure defining the
data centre boundaries
and energy flows
through the boundaries
and a formula for the
calculation of PUE using
different energy
carriers will help to
clarify calculation of
PUE
A figure similar to
Figure 4.3 in document
“Metrics for Net Zero
Energy Data Centres”
from the RenewIT
project (attached) or a
similar one can help.
See additional
document named “PUE
equations” for
suggested equations
and references to
energy conversion
factors
IREC Example Data Centre C,
starting line 589
Annex B. TE To be coherent, the
energy source entering
the data centre
boundary is natural
gas. So, assuming
electrical energy
efficiency in the
generator of 32%,
natural gas
consumption will be
15,625 kWh.
See document “PUE
equations”
IREC Example Data Centre D Annex B TE To be coherent, the
energy source entering
the data centre
boundary is natural
gas. So, assuming a
electrical energy
efficiency in the
generator of 32%,
natural gas
consumption will be
7,812.5 kWh.
See document “PUE
equations”
Figure and formulas
need to be changed
IREC Example Data Centre E Annex B TE Example A and Fig. B-5
can be eliminated to
avoid confusion
Delete Example A and
Fig. B-5.
For calculation in
Example B, Method 2;
see document “PUE
equations”
IREC Example Data Centre
with abosortion type
refrigerator
Annex B TE Example A and Fig. B-7
can be eliminated to
avoid confusion
Delete Example A and
Fig. B-7
For calculation in
Example B, Method 2;
see document “PUE
equations”
IREC Annex B TE I would suggest adding
an example with on-
site PV generation to
A proposal of simple
example is added in the
document “PUE

11
show how to calculate
PUE in that case.
equations”
2.2 Renewable Energies Factor (REF)
ISO/IEC JTC 1/SC 39 are close to finalizing the REF metric (ISO/IEC JTC 1/SC 39 REF, 2015). After careful consideration,
this has been selected as the Cluster methodology for measuring REF. Some considerations and comments to the
draft documents have been addressed by the Cluster using ISO/IEC templates through the French Committee. It
should be noted that this feedback was provided outside the normal request process and is as follows:
Member
Body
Location
Paragraph/Figure/
Table/Note
Comment
Type
Justification Proposed Change
GNF 5.1 Line 146 TE Renewable energy owned and
controlled by a data centre is
defined as any energy for which the
DC owns the legal right to the
environmental attributes of
renewable generation, which
includes for the reporting period in
question:
- Renewable generation
onsite, whose legal
rights are retired in the
DC.
- Renewable energy
certificates.
- Renewable energy
portion of utility
electricity, which shall
be counted, provided
the data centre has
obtained documented
written evidence from
the source utility
provider(s) that the
energy supplied, for the
reporting period in
question, complies with
the ISO/IEC definition of
renewable energy
described in clause
3.1.2.
Justification for change:
REF considers renewable energy
consumed in the DC according to
the renewable energy certificates
that can be obtained from the DC
energy supplier or from the
market. Using this procedure, Er
(Renewable energy used by the DC)
is not a real value, since renewable
energy is probably not being to be
consumed in the DC. We
understand that with this approach
there is a partially responsibility
transmission to a third party (REF):
certificates do not guarantee that
the DC is consuming less non-RES
Renewable energy is defined as any energy
for which the DC for the reporting period in
question as:
- Renewable generation onsite,
which is consumed in the DC.
- Renewable energy portion from
the grid, which shall be provided
by the energy supplier,
documenting written evidence
that the energy supplied, for the
reporting period in question,
complies with the ISO/IEC
definition of renewable energy
described in clause 3.1.2.

12
energy; this energy can be supplied
to any other consumer.
GNF 6.1 Line 168 TE “REF shall be an annualized value.”
Justification for change:
We agree that REF shall be an
annualized value, to consider
seasonal changes from the supply
and demand side point of view.
However, it is important not to use
aggregated values, but to evaluate
within the KPI. Seasonal, monthly,
daily and even hourly generation
may differ a lot from other time
scenarios.
“REF shall be an annualized value, and shall
be calculated as a summation of the usage of
renewable energies in the different time
intervals, as it can be seen in the formula.”
Where:
- EDC grid-cons i = DC energy
consumption from the grid during
the period of time i (kWh).
- Eren i/ Etot i = Renewable energy
portion from the grid (provided by
the energy supplier) in the period i
(kWh).
- EDC ren onsite i = DC energy
consumption from own renewable
energy production in the period of
time i (kWh).
- EDC i = Total amount of energy that
is consumed by the DC during the
period i (kWh).
Time interval considered for each i period will
depend on the degree of granularity, with
which energy supplier can provide renewable
energy portion from the grid (hourly,
monthly, etc). Level of granularity will
normally depend on the regulation
established to energy suppliers for informing
their customers.
2.3 IT Equipment Energy Efficiency (ITEE)
ISO/IEC JTC 1/SC 39 are close to finalizing the ITEE metric (ISO/IEC JTC 1/SC 39 ITEE, 2015). After careful
consideration, this has been selected as the Cluster methodology for measuring ITEE.
2.4 Cooling Effectiveness Rate (CER)
ISO/IEC JTC 1/SC 39 have just begun work on the CER metric and so this work is in early stages. The ISO/IEC 30134
(provisional) CER definition, discussed in the Cluster Task 3 documentation, has been selected as the Cluster
methodology for measuring REF. Some considerations and comments to the draft documents have been addressed
by the Cluster using ISO/IEC templates through the French Committee. It should be noted that this feedback was
provided outside the normal request process and is as follows:
Member
Body
Location
Paragraph/Figure/
Table/Note
Comment
Type
Justification Proposed Change
IREC 1 Scope GE As the CER is intended to
determine the performance of
the cooling system of a Data

13
Centre, one should define
which systems are included in
the definition of “the cooling
system” or “the cooling
infrastructure”. In section
6.1.1 (“requirements”), it is
stated that pumps, valves,
etc.. are included in all the
system. It is necessary to
clarify which kind of systems
are included as “cooling
infrastructure”, for example, if
water-chilled distribution
systems, air handling units, air-
cooled distribution systems,
buffer storage tanks, cooling
production machines (e.g,
vapor compressor chillers,
chillers, heat pumps,
absorption chillers, etc…)
Also, it is important to clarify
which are the limits of the
“cooling infrastructure” in the
production side. Some
examples can be given and
discussed. For example:
 In the case of a gas
fired absorption
chiller, is the boiler
part of “the cooling
system”?
 In the case of a
Data Centre
connected to a
District Cooling
network, is “the
cooling system”
starts at the
substation
connection system
with the network?
 Are energy
renewable
production systems
integrated in the
Data Centre
technical systems
(e.g. PV solar or
solar thermal
systems)
considered part of
“the cooling
systems”?
IREC 3.1.1 Line 89 TE Use the same name “Cooling
Efficiency Ratio” to define
both the “instantaneous /
actual” measurement and the
ratio over a period of time
(seasonal)
Seasonal Cooling Efficiency Ratio (SCER)
IREC 3.1.2 Line 92 TE Use the same name “Cooling
Efficiency Ratio” to define
both the “instantaneous /
actual” measurement and the
Cooling Efficiency Ratio (CER)

14
ratio over a period of time
(seasonal)
IREC 5.3 Line 144 TE Definition of SCER
IREC 5.3 Line 146 TE Units should differentiate
thermal energy and electrical
energy
But in kWhth/kWhel
IREC 5.3 Equation 2 TE Mathematical notation with
integral notation and units
IREC 5.4 Line 150 TE Definition of CER
IREC 5,4 Line 151 TE Change COP by EER …into the direction of a EER for cooling
infrastructure.
IREC A.3 Line 273-276 TE It is stated that infrastructure
to distribute heat in a building
is not considered as a part of
the data centre (and then, not
part of the cooling
infrastructure either). But,
within the following sentence,
it is stated that energy to
transfer heat out of the Data
Centre shall be accounted.
Better explanation or
clarification is needed.
IREC A.4 Line 278 TE Change CPR by CER Using CER in Capacity Management
IREC TE In relationship with first
general comment, an example
of a non-electrical driven (or
partially non-electrical driven)
cooling system, (i.e. a gas fired
absorption chiller) should be
provided. For these cases, an
equivalent electrical CER can
be defined considering the
performance of thermal driven
cooling machines,
performance of thermal
generators and conversion
factor between the energy
carrier and electricity.
Add some example of non-electrical driven
cooling system
2.5 Energy Reuse Effectiveness (ERE)
In contrast with other metrics of this category presented thus far, and to the best of our knowledge, there is
currently no ISO/IEC JTC 1/SC 39 dedicated to this metric. As such and for the purposes of this Cluster, in the
following we consolidate information related to this metric in our effort to improve its feasibility and applicability by
our project’s validation pilots.
2.5.1 M & V Plan Scope and Metric Overview
This section describes in detail the Energy Reuse Effectiveness (ERE) metric, as initially suggested by The Green Grid
(The Green Grid, 2010). As stated in the white paper that introduced the metric, its goal is to capture and measure
the benefit of reusing any recovered energy from the data centre. In terms of applicable use cases, the focus lies on
heat reuse.
It should be noted that this metric focuses on energy being captured from within the data centre and reused outside
of its premises, since the benefits of reusing energy within the same data centre are captured by the Power Usage
Effectiveness (PUE). Computing and analysing both metrics can thus provide better insights into the data centre’s
energy recovering strategies in terms of both capabilities and opportunities.

15
Its formula, following the same line of thought as in the case of PUE (ISO/IEC JTC 1/SC 39 PUE, 2015), is defined as
follows:
(1)
As reused energy is essentially in the form of heat, energy source weighting factors should be applied. Section
2.5.2.1 provides details on such factors.
All values are units of energy, while the metric itself is just a number ranging from 0 to infinity. A value of 0 means
that 100% of the energy brought into the data centre is reused elsewhere, outside of the data centre boundaries.
One should therefore imagine a boundary line that defines the data centre’s co-called Control Volume (CV), the area
enclosing all data centre facilities and support infrastructure. For the purposes of this metric, energy crossing this
line should be accounted for. For an illustration of the CV concept, you can refer to section 2.5.2 of this document,
where a typical scenario is outlined in detail.
There is an alternate formula to compute the ERE metric based on PUE while introducing the Energy Reuse Factor
(ERF) as the portion of energy that is exported for reuse outside of the data centre. Its formula is defined as follows:
(2)
Equation (1) can be thus expressed as:
(3)
with ERF lying within [0,1]. A value of 0 means no energy is being exported from the data centre for reuse, while a
value of 1 means that the amount of energy brought into the data centre equals to the amount that is being reused
outside of the data centre. For more background information on this metric please refer to the (The Green Grid,
2010) whitepaper; a concise description of the ERF is also found within the report on harmonizing global metrics for
data centre energy efficiency (Global TaskForce, 2014).
For all intents and purposes of this document, definitions pertaining to commonly used terms are those specified
within ISO/IEC JTC 1/SC392
documents, unless otherwise explicitly stated. In addition, parameters of this metric
already defined and evaluated in the aforementioned documents, such as PUE and total energy consumption, are
measured and computed following the same methodology.
In the following we elaborate further on measurement specifications special to this metric and in particular related
to energy being reused, and provide concrete steps to analyse, validate, and report its values.
2.5.2 Measurements
2.5.2.1 Measurable variables determination
In order to compute a value of the ERE metric, provided that the corresponding PUE value is known, one just needs
to measure the energy that is being reused outside of the data centre. In this case the total energy consumption of
the date centre facility is assumed to be known, being one of the parameters necessary to compute the PUE.
2
http://www.iso.org/iso/home/standards_development/list_of_iso_technical_committees/iso_technical_committee.htm?com
mid=654019

16
Figure 1 illustrates a typical scenario of reusing recovered heat from the data centre. In this example, the total
energy consumed by the data centre is measured at the Point of Delivery (POD) and assuming negligible
transmission losses this would be the sum of energy coming from the grid, A, plus energy that may be produced and
consumed onsite, B. Assuming the latter represents electricity generation on site, it is proposed to include the
quantity of electricity, measured at B, and not the source energy associated with the generation. This can be
considered as drawing the CV to exclude generation onsite, however the units may also function as standby
generation capacity, in which case they should be included, for the purposes of heating, parasitic load, and so on.
The joint Green Grid and ASHRAE publication on PUE (The Green Grid / ASHRAE, 2013) excludes generator efficiency
effects by introducing an additional internal IT Source factor for IT electricity; we propose a simpler method of
metering the energy generated and adding it to the energy taken from grid so as to get the total energy input.
In this example, the PUE3
would then be computed as:
(4)
Figure 1: Reuse of recovered heat from the data centre
Heat recovered by the data centre, H, may represent either hot air or water that is being fed into a heat pump and
further used by an external facility such as a nearby greenhouse, or the local/district heating grid. Based on ERF’s
definition by Equation (2), its value would then be:
(5)
with denoting the energy source weighting factor.
3
In this example, we assume the more advanced PUE category (3), where the IT load is measured at the IT equipment input.

17
All units are in kWh since the majority of data centres operate on electricity; as explained in detail within the report
(Global TaskForce, 2014), the energy from each energy type should first be converted into kWh, then multiplied by
the weighting factor for the type, and finally the source energy for all types can be added together. Table 1 lists the
global average weighting factors per (typical) energy type, which may be used when more exact regional values are
not available4
.
4
As of writing, the factors reported within In order to compute a value of the ERE metric, provided that the
corresponding PUE value is known, one just needs to measure the energy that is being reused outside of the data
centre. In this case the total energy consumption of the date centre facility is assumed to be known, being one of the
parameters necessary to compute the PUE.
Figure 1 illustrates a typical scenario of reusing recovered heat from the data centre. In this example, the total
energy consumed by the data centre is measured at the Point of Delivery (POD) and assuming negligible
transmission losses this would be the sum of energy coming from the grid, A, plus energy that may be produced and
consumed onsite, B. Assuming the latter represents electricity generation on site, it is proposed to include the
quantity of electricity, measured at B, and not the source energy associated with the generation. This can be
considered as drawing the CV to exclude generation onsite, however the units may also function as standby
generation capacity, in which case they should be included, for the purposes of heating, parasitic load, and so on.
The joint Green Grid and ASHRAE publication on PUE excludes generator efficiency effects by introducing an
additional internal IT Source factor for IT electricity; we propose a simpler method of metering the energy generated
and adding it to the energy taken from grid so as to get the total energy input.
In this example, the PUE would then be computed as:
(4)
Figure 1: Reuse of recovered heat from the data centre

18
Table 1: Global average source energy weighting factors (Source: (Global TaskForce, 2014))
Energy type Weighting factor
Electricity 1.0
Gas (Natural gas) 0.35
Fuel oil 0.35
Other fuels 0.35
District chilled water 0.4
District hot water 0.4
District steam 0.4
As already implied by the discussion so far, this metric is data centre centric. In this sense, therefore, one does not
need to include in the computations pertaining to this metric the waste as a result of heat reused by the external
facility, J. That being said, one should ensure in advance that the business case for reusing recovered energy outside
the data centre’s premises is economically viable and environmentally sustainable: the resulted system’s total
energy consumption should be less when reusing energy.
In the remainder of this section we provide details on measurement points, metering equipment and assumptions,
along with guidelines on how to adequately identify a baseline scenario and possibly necessary adjustments. The
latter would be as part of the exercise to better capture, understand and analyse the energy recovered strategies
put in place along with opportunities for further improvements.
2.5.2.2 Baseline identification and calculation
To compute ERE no baseline identification is needed. However, an important step towards assessing the added value
of energy recovered from the data centre involves comparing the metric’s values between two different periods of
time. Such a comparison would then be made between the value over the period before implementing the measures
to recover energy, namely baseline, and the period following once improvements have been applied.
Baseline scenario must be representative of a typical operational period, normally being a year as thermal needs are
seasonal. Therefore, the base-year conditions refer to the knowledge base of the state of various data centre aspects
Heat recovered by the data centre, H, may represent either hot air or water that is being fed into a heat pump and
further used by an external facility such as a nearby greenhouse, or the local/district heating grid. Based on ERF’s
definition by Equation (2), its value would then be:
(5)
with denoting the energy source weighting factor.
All units are in kWh since the majority of data centres operate on electricity; as explained in detail within the report,
the energy from each energy type should first be converted into kWh, then multiplied by the weighting factor for the
type, and finally the source energy for all types can be added together. Table 1 lists the global average weighting
factors per (typical) energy type, which may be used when more exact regional values are not available.
Table 1 are in line with industry normal values, defined by The Green Grid, and due for inclusion in an updated version of the
ERE definition document. It should be noted, however, that other European directives, particularly the Energy Performance of
Buildings Directive (EPBD), utilise a different format, termed Primary Energy Factors. Whereas source energy factors use grid
electricity as the base unity value (=1), primary energy factors use fossil fuel = 1, with electricity varying depending on grid
electricity makeup.

19
during 12 months preceding the decision to deploy and use energy reuse infrastructure. In order to evaluate the
benefits of heat recovery, or changes to heat recovery, it is necessary to establish a baseline. This will require
gathering data over a range of operating conditions: IT loading, ambient weather conditions (seasonal variation) and
heat sink variations in load, temperature and flow, which is representative of the intended operating envelope.
Ideally this would involve at least 12 months with stable operating loads; in practice this may be difficult to attain
and a pragmatic approach should be adopted. For example, where heat recovery is considered beneficial during the
winter months only (heating season), then data from September – April may be considered adequate.
In the following, we divide this knowledge set into two categories, one pertaining information critical to enabling
accurate identification of the data centre’s baseline conditions and a second one, related to information that could
prove to be useful for enabling more fine-grained baseline identification, however affecting only slightly the resulting
baseline profile.
Collection and analysis of such data may allow us to compare variations in the ERE metric between these two
periods of time, eliminating to the degree possible the distortion effect on energy consumption introduced by such
variations – as already discussed in section 2.5.2.3.
The underlying assumption is that a data centre operator will normally be familiar with the main parameters that
affect data centre energy consumption. A detailed, orientative list is presented below, including the most common
parameters that relevantly affect data centre energy consumption, which would be mandatory to collect:
 Electrical meter data, preferably for short time intervals (between 15 minutes and 1 hour, being every 30
minutes a good approach) for all factors necessary for PUE, ERE, and ERF calculation
 Heat flow data across control volume: Medium, Humidity (for air), Flow, and Flow and Return Temperatures
 Temperatures of IT Whitespace (CRAC/CRAH inlet and delivery) and Ambient and Heat sink
 Confirmation that there has been no significant change in cooling plant configuration or operating
philosophy over the baseline period
 A lighting levels investigation
 A detailed report on the number and (static) energy characteristics of IT equipment (both for computing,
networking, and storage purposes) along with the respective energy consumption measurements, wherever
available
 A detailed report on the number, and (static) energy characteristics of non-IT equipment (HVAC, lighting, and
so on) along with the respective energy consumption measurements, wherever available
 A record of the temperature setting of cooling equipment
 A report of the number, type and (static) energy characteristics of energy-reuse devices
 A record of the number of working days and hours for each month of the year
 A summary of detailed weather and climatic data for each month of the year
 A report on the number, type, position and error of metering devices
 A record of energy-efficiency techniques in place
In addition to these fundamental sets of data, the following pertain to information that could prove to be useful for
fine-tuning the data evaluation for the baseline identification if available, but are considered as too costly or too
hard to obtain;
 More fine-grained reports regarding IT consumption behaviour of the various fundamental IT equipment
elements (CPU, RAM, HDD, Routers, Switches). In case a DCIM is used for monitoring and controlling the DC,
this information will most probably be available and should be used to identify the baseline.

20
 More fine-grained reports of the components of the HVAC equipment (air conditioning, heating, lighting,
and so on) if the installation of a sufficient number of meters is not considered too costly, or a monitoring
framework is in place.
Bearing in mind the connection of this metric to PUE, for the aforementioned measurable entities, the sampling
period for measurements pertaining to dynamic parameters should be the same for both metrics. The base-year
energy use is metered at POD of Figure 1 spanning at least a 12-month period5
. The base-year energy reuse is
metered at point H of Figure 1 spanning the same period as the one defined for the case of base energy use.
The base-year energy data should be analysed as follows. A mathematical model (e.g. linear regression) shall be
applied on representative period energy use and demand, IT load, metering period length, and degree days. The
latter shall be derived by third party information providers. Correlation of weather with energy demand, supply and
use is expected to be identified.
Benefits along with potential improvements derived from energy reuse will be determined under post-retrofit
conditions.
2.5.2.3 Baseline adjustments in case of anticipated changes
Baseline adjustments are needed to bring base-year energy use to the conditions of the post-retrofit period. The
method to measure and verify should be broadly in accordance with IPMVP Option C. Nevertheless, since formula (3)
is suggested to be used, then and bearing in mind that we measure PUE (IT energy consumption), the IPMVP Option
will be a mix of C and B.
2.5.2.3.1 Routine Adjustments
2.5.2.3.1.1 Electricity Consumption
At least, the electricity consumption is expected to be affected by the number of operating days and the weather. As
result, the following routine adjustments are normally needed:
 The cooling equipment consumption may vary depending on the ambient temperature. Appropriate
adjustments should be made, based on manufacturer’s sheets.
 The electricity consumption of air-conditioning and heating facilities should be adjusted to ambient
temperature based on manufacturers’ specifications.
2.5.2.3.1.2 Thermal Energy Waste
The waste heat may be used either to “heat” or “cool” external facilities. Adjustments on thermal energy waste may
be needed with respect to the number of operating days and hours, as well as the ambient temperature. Such
adjustments may be conducted following the specifications of the IT equipment manufacturers.
2.5.2.3.2 Electricity Demand
Adjustments on electricity demand may be needed, since the ambient temperature and daylight hours affect the
cooling, heating and lighting demand within the data centre.
In the cases that IT services offered to the customers have a relevant variation from year to year adjustments should
be applied.
2.5.2.3.2.1 Thermal Energy Demand
N/A
5
Ideally, integral multiples of 12-month periods (12, 24, 36 months, and so on) should be considered, in order to alleviate the
effects of seasonal DC workload variations.

21
2.5.2.3.3 Non-routine Adjustments
Procurement of new equipment during the post retrofit period raises the need for non-routine adjustments. The
new equipment may refer to IT (CPU, RAM, HDD, and networking devices), non-IT (cooling equipment), and facilities
(air-conditioning units, heating, and lighting). The adjustments should include calibrating the potential extra energy
consumption to the base-year one, referring to manufacturers’ factsheets on energy consumption.
2.5.2.4 Measurement boundaries determination & metering points
As already mentioned for this metric, being data centre centric, is not within scope to take into account whether an
equipment outside of the data centre’s premises is efficient or not. To that end, Coefficient of Performance (COP) of
external to the data centre heat pumps should not be considered. The energy reused should be measured exactly at
the point where it leaves the CV of the data centre. Nevertheless, the system’s total energy consumption should be
less when reusing energy.
On the other hand, cases where waste heat may be used, internally, to preheat generators for data centres
electricity production is also out of scope of ERE. In addition, electrical energy stored onsite for later use, including
possibly provision to external facilities, is considered out of scope of this metric, since stored energy is already
accounted for when measured at POD and in this context cannot be considered energy recovered.
2.5.2.4.1 Total energy consumption
Both the base-year and post-retrofit energy use for Equation (3) is taken directly from the metering equipment at
POD of Figure 1 without adjustment. Same guidelines as the ones related to PUE computation are to be followed.
2.5.2.4.2 Energy Reuse
Similarly both the base-year and post retro-fit energy reuse for Equation (3) is taken directly from the metering
equipment at point I of Figure 1 without adjustment.
2.5.2.5 Metering equipment desired characteristics/capabilities (HW / SW)
Metering equipment should be able to track kWh from the desired circuit.
The output from the main metering options should be made available on a web portal for ease of use and for easy
dissemination of information.
The data capture can be from:
 existing ‘micro’ meters already in situ where the data is sent via the communication unit to the web portal;
 new CT clamps on wiring linked to communication unit which will feed the web portal; and
 temporary data loggers with CTs around specific circuit wiring and data caught locally and then uploaded.
2.5.2.6 Metering equipment commissioning procedure
2.5.2.6.1 Electricity
The DC circuitry should be fully analysed.
A plan of what circuits to be measured should be assembled.
In order to ascertain the most basic KPI (EnPI), PUE, and continuing with the assumption of the more advanced PUE
category, a meter is required at POD and at (G), the latter denoting the point of measurement for the energy
consumed by the IT equipment6
, as shown in Figure 1. Additional metering will add to the complexity of data
obtained and help with the other KPIs.
6
For details on PUE measurement, please refer to (ISO/IEC JTC 1/SC 39 PUE, 2015).

22
It may be necessary to change some circuitry so that all the cooling, say, is captured on the one meter. If this is not
possible, then a schematic of the system should be drawn up and additional meters placed so as to capture all of the
necessary data for that specific energy use. Careful consideration must be taken so that energy is not missed nor
counted twice as this will cause significant problems with calculations.
2.5.2.6.2 Thermal
It will be necessary to capture the quantity, namely the volume, and quality, the temperature, of air being exhausted
or water being transferred. As such, meters other than kWh will have to be employed.
Flow meters will be necessary to calculate the volume. These will produce an output that should be captured by the
main monitoring system and sent via the communication unit to the web-portal.
In addition to the volume, the temperature of the air (both inside the data centre and ambient external) or the
water, will have to be captured. In particular, for water systems flow and return temperatures will be required.
In addition, one should record ambient temperature, air exhaust from data centre, and temperature of heated space
or district heating circuit.
Again, this information should be captured by the main data-capture system and sent via the communication unit to
the web-portal.
2.5.2.7 Metering assumptions
Energy lost through the fabric of the building is ignored and not measured. Steps to minimise these should be taken,
but not measured.
Capture of the quantity of air and its temperature will allow the calculation of the energy that the air contains. It will
be necessary to elaborate how this should be done.
2.5.2.8 Metering sampling frequency
The data sampling frequency should facilitate calculations to give an accurate picture of the energy use in the data
centre. If the data centre energy use and processing speeds are very constant, then low sample rates may suffice.
However, if very rapid changes are observed, then a higher sampling rate may be required.
For example, in the data centres that Google operate, they collect these types of data every second. As such they
have 86,000 data points for every meter every day. This level may be too fine for some applications.
However, hourly data points may be too coarse. So a suitable medium position may be required.
A sample rate of, for example, between every 30 seconds and every 10 minutes will probably allow a good data set
from which to first start. Adjustments up or down from this may be necessary after the first tranche of data is
analysed. In general, sampling frequency should not be too short so as to avoid noise from actions of control
systems. Aggregating to periods of 1 hr or 1 day for modelling and metric calculation could be a good compromise.
Typical values of sample rate per parameter are listed in Table 2.
Table 2: Typical values of sample rate per parameter.
Level of Granularity Parameter Interval (min*)
Server room (non – IT)
Air temperature 5 – 10
Chilled water flow rate 0.5 – 1
Chilled water temperature 5 – 10
Relative humidity 5 – 10
Server room (IT) CPU utilisation % 0.5 – 1

23
Networking utilisation7
% 0.5 – 1
Storage utilisation8
% 0.5 – 1
Server room exit
Air flow rate 0.5 – 1
Air temperature 5 – 10
Electricity
Main incomer 0.5 – 10
UPS 0.5 – 10
Server room incomer 0.5 – 10
General
Degree days cooling 1 day
Outside dew point 1 hour
2.5.2.9 Metering duration (post-retrofit period)
The duration of the measurement should be at least over 12 months, or at least encompass both summer and winter
conditions. This will allow the analysis over a full cooling (and heating) season.
It will be preferable to allow 3 full years of data to show any annual trends. This will give a more robust result of the
system.
7
Current switching throughput / Max switching throughput
8
((used storage capacity/maximum storage capacity) + (data transfer/max data transfer capacity))/2

24
3 Task 4.2 – Methodologies for new Metrics
The purpose of Task 4.2 was to define methodologies for new metrics selected by the Smart City Cluster
Collaboration during Task 3. Hence, the work for this task involved considering methodologies most suitable for use
by the cluster and extending known methodologies.
The methodologies hereby defined should be viewed as a first attempt in tackling the introduction of such a global,
environmental family of metrics for the data centre. As such, the document describes the proposed approach simply
as a step-by-step guideline the data centre operator may follow to measure the necessary parameters for computing
the selected metric(s). The project-members of the cluster will put this approach into practice during their pilot
trials. In doing so, insights will be obtained to refine the proposed methodology as necessary.
Metrics within Task 4.2 are classified into three categories:
 Flexibility Mechanisms in Data Centres - Energy Shifting, including metrics such as Adaptability Power Curve
(APC), Adaptability Power Curve at Renewable Energies (APC_REN), and Data Centre Adapt (DCA)
 Savings family of metrics including Primary Energy Savings (PE Savings), CO2 avoided emissions (CO2
Savings), and Energy Expenses (EES),
 Renewables integration - Energy produced locally and Renewable usage with the Grid Utilization Factor
(GUF) metric.
3.1 Adaptability Power Curve
The present section is aimed to provide the measurement and verification methodology for the APC metric,
following the International Performance Measurement and Verification Protocol (IPMVP).
The APC metric belongs to the category of “Flexibility mechanisms in Data Centres: Energy shifting”, presented in
“Cluster Activities Task 3” document (§2.2.1).
This metric assumes that an energy usage pattern is in place, to which the data centre must adapt to the greatest
extent possible. The energy plan may be provided by an Energy Managing Entity within the Smart City or the Smart
grid or by the DC itself, as a result of self-optimization policies. APC aims at measuring the degree of adaptation of
the DC energy consumption to a planned energy curve.
APC is given by the following formula:
(1)
(2)
Where:
is the DC energy consumption in kWh;
is the planned energy in kWh;
is the individual time period
represents the sample size and
is the adjustment factor between and normalising the two energy consumption curves.

25
To specify better, the a priori defined cannot take into account possible changes in current conditions and the
incurring variations in current . To eliminate the effect of these variations on the metric performance
scales the planned energy at the level of the energy consumption , i.e. the two curves must subtend the
same area in order to have the same total energy and be therefore comparable.
As derived from eq. (1), APC values are unit-less where 1.0 corresponds to full adaptation. The lower the adaptation
between both curves is, the lower the value achieved for APC (very different curves can cause even APC negative
values). In order to calculate the APC values, the planned and actual DC energy usage have to be provided; the
former is calculated or provided, while the latter is measured.
3.1.2 Measurements
According to the formula aforementioned, the only parameter to be measured is the total energy consumption of
the data centre for each time interval in kWh. More information on the selected timeframe and the baseline
scenario is found within sections 3.1.2.2 and 3.1.2.3. For details on measurement points please refer to section
3.1.2.4.
is not measured, as its values are predetermined by the DC management or some other entity. and are
energy consumptions produced at the same time (simultaneous), i.e. is the energy consumed by the DC after
trying to adapt its consumption to the demand order (
It must be noted that, unlike other metrics, no independent variables/static factors are needed to be measured, as
only the profiles of the curves are compared.
Baseline is not applicable to this metric.
As no baseline is applicable to this metric, no adjustments are required.
Figure 2: Data Centre Control Volume and Measurement Points illustrates a general scenario of a data centre. The
total energy consumption of such a data centre is measured at the Point of Delivery (POD) and is the summation of
energy coming from the utility (A) plus energy generated onsite (B); both measurements are in kWh. This means that
all types of energy are considered, both primary (e.g. fuel for an onsite generation engine) and secondary, and
converted in electricity.

26
Figure 2: Data Centre Control Volume and Measurement Points
However, it is worth highlighting that particular cases in which the energy plan provided to the Data Centres does
not include onsite production could happen. For example, in the case that there is a restriction or a demand order
only for the electricity consumed from the grid. In that case, energy consumption to consider will have to be
measured at the meter from the utility (A).
All the required variables can be metered using permanent energy meters, installed on the metering points
highlighted in the previous paragraph. Those energy meters should comply with the following requirements.
- Meters range must be consistent with the metered variables range and these meters should allow a
consistent unit selection.
- Meters must be equipped with a communication module (Modbus RTU RS485 protocol or equivalent), must
be connected to a gateway that via Ethernet allows concentrating data that are stored in log files or in a
database. A properly sized storage system must be designed and installed and data must be available for the
next phase of analysis and verification. In this way, each meter, becomes a node of the network.
Meters must be equipped with the required auxiliary devices such as amperometric transformers, voltmetric
commutators, surge protectors and surge arresters.
- The choice of measurement boundaries and metering points must be performed from the Data Centre
perspective. Moreover meters and auxiliary devices must be chosen according to the supply voltage. For this
reason, LV point of measures must be preferred to MV point of measures, also due to lower metering costs.
Measurement errors (defined by the HW’s accuracy classes), connections, communication protocols and networking
must be compliant with the existing standards (ANSI, IEC, IEEE, CEI EN) and national regulations.
The commissioning process assumes that owners, programmers, designers, contractors, operations and
maintenance entities are accountable for the quality of their work. Commissioning process includes several

27
procedures that are required in order to ensure the adequacy and the degree of precision required for the quality of
measurement and the product safety.
Once the measurement system is installed, a test procedure must be performed. During this procedure, once the
meter settings are set, the measure collected by the installed meter is compared with the measure collected through
a portable configured meter. The test procedure must not be confused with the calibration procedure performed by
the manufacturer. The test procedure includes the test of the communication channels (speed and reliability) and
the sampling time.
Together with the test procedure a maintenance procedure is required. The key tests required are:
- Every time the gateway does not communicate with the storage system or one or more nodes do not
communicate with the gateway it is necessary to check and solve the problems in due time.
- Periodically (once a year) it is important to inspect the measurement equipment and repeat the calibration
procedure.
- Every time hardware changes occur, the compliance between the meter range and the physical variable
range must to be checked and guaranteed.
If the commissioning procedure is workmanlike performed a maintenance procedure is not indispensable
As APC is based on comparing the total energy consumption of a DC to acomputed (optimal) energy consumption
curve, the main assumptions will be that Sections 3.1.2.1, 3.1.2.4 and 3.1.2.5 provide all the necessary information
(metering equipment, location) to measure the total energy consumption of the DC. All meters are assumed to be
calibrated, commissioned and tested, so the energy consumption measurement values are accurate and rigorously
collected and the samples are representative. If these assumptions are not met, an analysis to detect which
measurements are being missed or are not being considered should be made, and a procedure to include them with
new equipment or with estimations must be applied, including quantifying all the uncertainties added in case of
estimating any consumptions.
In case of changes in the metering equipment (including removal or plain change) during the sampling period, it is
assumed that the M&V plan describes all the specifications and calibration requirements and locations of the
metering equipment in order to continue the close as possible to the same metering scenario.
The sampling frequency will have dependency on hardware and software requirements (granularity of the meters,
data storage limitations, SCADA or Software limitations etc.); to capture the energy consumption pattern a
frequency in the order of minutes should be considered. Low measuring frequencies introduce the risk of not
capturing energy consumption peaks, reducing the effectiveness of the energy consumption behaviour capturing. To
this end, a measurement every 1 to 5 minutes would be recommendable with a maximum period not exceeding 15
minutes. In any case, the optimal energy consumption behaviour calculation should follow the DC energy
consumption measurements period and vice versa, in order to avoid unnecessary, overhead computational and
metering load.
In practice, a measurement period of 15 minutes (96 measures per day) is a good approach, providing a clear picture
of the daily energy pattern consumption of a DC, being adequate for the creation of different energy baselines.
The pre- and post- implementation period should be measured with a similar period length and conducted using the
same procedure (equipment, sensor location, etc.).

28
As energy consumption depends on weather conditions, the measurements should include all the different seasons
and all different weather conditions: for this reason whole year duration is recommended. Similarly, variable DC
workload and usage patterns should be contemplated. For example, University or office building DCs will have
clearly less workload in summer or during holiday periods. Taking into consideration all the above, a post-retrofit
period of at least a year is the best option to capture DCs energy behaviour.
The minimum period must be one that sufficiently covers a wide range of weather and usage conditions. In this case
specific metering duration periods will be selected, depending on usage and location of the DC, in order to give a fair
representation of the DC behaviour.
3.1.3 Example
Indicatively, Figure 3 presents an example calculation of the APC flexibility metric for a hypothetical DC. Specifically,
assuming that the measurement procedures described in the previous paragraphs were respected, Figure 3 depicts
the measured DC energy consumption , versus the planned energy consumption, for 6 consecutive time
intervals, i = 1,..,6.
In the example assumed, the adjustment factor equals . Therefore, APC can be
calculated as follows:
(3)
Figure 3: DC energy consumption, and planned energy consumption, over time

29
3.2 Adaptability Power Curve at Renewable Energies
The present section is aimed to provide the measurement and verification methodology for the APCREN metric,
following the International Performance Measurement and Verification Protocol (IPMVP).
The APCREN metric belongs to the category of “Flexibility mechanisms in Data Centres: Energy shifting”, presented in
“Cluster Activities Task 3” document (§2.2.2).
This metric assumes that a renewable energy availability is provided, to which the data centre must adapt to the
greatest extent possible. The energy plan may be provided by an Energy Managing Entity within the Smart City or
the Smart grid or by the DC itself, as a result of self-optimization policies. APCREN aims at measuring the degree of
adaptation of the DC energy consumption to a planned renewable energy curve.
APCREN is given by the following formula:
(4)
(5)
where:
is the DC energy consumption in kWh;
is the available renewable energy (to be consumed) in kWh;
is the individual time period
 represents the sample size and
is the adjustment factor between and .
Specifically, as accounts for all available renewable energy, its order of magnitude will generally be higher
than that of . The opposite is unlikely. In this course, allows the correlation of both variables providing
information - on the adaptability of the power curve - which otherwise would be suppressed by the difference in the
order of magnitude.
As derived from eq.(4), APCREN values are unit-less, where 1 corresponds to full adaptation. The lower the adaptation
between both curves is, the lower the value achieved for APCren (very different curves can result to negative APCren
values).
3.2.2 Measurements
According to the formula aforementioned the parameters to be measured are:
 total energy consumption of the DC for each time interval i expressed in kWh;
 total energy coming from renewable sources, taking into account both the onsite generation and the energy
purchased on meter at time instant i expressed in kWh. In the case of primary energy containing a
percentage of energy coming from renewable sources, the calculation of the absolute value of purchased
renewable energy should derive as the multiplication of this percentage with the total energy purchased.
For details on measurement points please refer to section3.2.2.4.

30
Baseline is not applicable to this metric.
Baseline is not applicable to this metric and therefore no baseline adjustment is required.
Figure 4: DC Control Volume and Measurement Points illustrates a general scenario of a DC. The total energy
consumption of such a DC is measured at the Point of Delivery (POD) and it would be the summation of energy
coming from the utility (A) plus energy generated onsite (B); both measurements would be in kWh. This means that
all types of energy are considered, both primary and secondary, and converted in electricity.
The energy coming from renewable sources is the summation of energy produced locally BonsiteRes plus the
percentage, if any, purchased from the utility.
Figure 4: DC Control Volume and Measurement Points
database. A properly sized storage system has to be designed and installed and data must be available for
the next phase of analysis and verification. In this way, each meter, becomes a node of the network.

31
Finally, measurement errors (defined by the HW’s accuracy classes), connections, communication protocols and
networking must be compliant with the existing standards (ANSI, IEC, IEEE, CEI EN) and national regulations. The
requirements specified in these standards and regulations have to be considered as minimum values for the
meters under normal working conditions. For special application, higher constraints might be necessary and
should be agreed between the user and the manufacturer.
the sampling time.
 Every time the gateway does not communicate with the storage system or one or more nodes do not
 Periodically (once a year) it is important to inspect the measurement equipment and repeat the calibration
procedure.
 Every time hardware changes occur, the compliance between the meter range and the physical variable
If the commissioning procedure is workmanlike performed a maintenance procedure is not indispensable.
As APCREN is based on comparing the total energy consumption of a DC to the total energy provided by renewable
energy sources (RES availability from the grid and local generation), the main assumptions will be that Sections
3.2.2.1, 3.2.2.4 and 3.2.2.5 provide all the necessary information (metering equipment, location) to measure the
total energy consumption of the DC. All meters are assumed to be calibrated, commissioned and tested, so the
energy consumption measurement values are accurate and rigorously collected and the samples are representative.
For renewable energy availability curve the assumptions will be that the curve is produced taking into consideration
all the onsite renewable power systems that feed the DC, and that the energy provided by renewable systems has
been accurately measured. A procedure to measure the grid energy share that comes from renewables (RES
certificate) shall be established with a timeframe in the same order of the measurements provided by
measurements in the onsite installation.
If these assumptions are not met, an analysis to detect which measurements are being missed or are not being
considered should be made, and a procedure to include them with new equipment or with estimations must be
applied, including quantifying all the uncertainties added in case of estimating any consumptions.

32
data storage limitations, SCADA or Software limitations etc.); to capture the energy consumption pattern a
minutes.
In the case of renewable energy production, a higher measurement frequency is needed to cope with the
intermittent nature of the RES production. Indicatively, solar or wind energy production are extremely variable; a
recommending measurement period would be 1 minute. In any case, the measurements period regarding RES
should follow the DC energy consumption measurements period and vice versa, in order to avoid unnecessary,
overhead computational and metering load.
of the daily energy pattern consumption of a DC, being adequate for the creation of different energy baselines. For
renewable energy production measurements, 15 minute are too high and higher sampling rates in the order of 30
seconds to 1 minute would be necessary to capture the renewable energy contribution.
Summing up, a recommendable sample time would be 1 minute and the sample time shouldn’t be higher than 15
minutes.
and all different weather conditions: for this reason whole year duration is recommended. Similarly, variable DC
specific metering duration periods will be selected, depending on usage and location of the DC, in order to give a fair
representation of the DC behaviour.
3.2.3 Example
Indicatively, Figure 5 presents an example calculation of the APC_REN flexibility metric for a hypothetical DC.
Specifically, assuming that the measurement procedures described in the following paragraphs have been respected,
Figure 5 depicts the measured DC energy consumption, , versus the available renewable energy, at 6
consecutive time intervals, i = 1,..,6.
In the example assumed, the adjustment factor equals Therefore, can
be calculated as follows:
(6)

33
Figure 5: DC energy consumption, E_DC and the available renewable energy, E_Ren over time
3.3 Data Centre Adapt
This section presents the measurement and verification plan pertaining to the newly proposed metric Data Centre
Adapt (DCA). This metric provides information on how much the energy profile of a data centre has shifted from a
baseline energy consumption after the implementation of flexibility mechanisms has taken place. By flexibility
mechanisms we refer to strategies, policies or in general sets of actions, such as employing workload management
techniques, in an effort to adapt the data centre’s energy consumption as much as possible to a planned energy
curve. Ideally, the planned energy curve is one that represents running the data centre in a more energy efficient
mode. This metric measures the change of the energy consumption curve.
It is important to distinguish DCA from a related energy shifting metric, namely APC. The latter compares a planned
curve, suggested for example by an energy manager in order to modify the data centre’s energy consumption
according to energy optimizations, constraints, or both, and its actual final consumption, modified in an effort to
follow the suggested energy curve. The information then provided by this indicator is the data centre’s capability to
adapt its consumption as in a demand response paradigm. Although the planned energy curve is determined
previously, information given by both curves belongs to the same time period.
Conversely, DCA provides information about the flexibility that has been achieved after implementing actions to
adapt the data centre’s energy consumption during certain periods of time to energy profiles that are selected as
more advantageous, if compared with the energy profiles operating before. The reason for the changes will normally
be the adaptation of the consumption to a planned power curve (devised autonomously or provided by a smart grid
15
18
13
19 18 17
10
19
98
83
18
22
0
20
40
60
80
100
120
1 2 3 4 5 6
EnergyConsumption(MWh)
Time Period i
EDC ERen

34
authority). However, this metric provides no information about the accuracy level of this adaptation; it rather
focuses on the degree of flexibility achieved due to the modifications in the operation approaches of the data centre.
These two curves, necessary to compute DCA, are not simultaneous in time. Therefore, information on the data
centre before and after implementing changes is required.
The equation below denotes the general mathematical formula to compute this metric:
(7)
is a scaling factor to render comparable the involved energy consumption curves. In particular, due to the
inclusion of the flexibility mechanisms, variations in the global energy consumption may occur. Moreover, variations
due to services provided, outside temperature, and so on, may appear as baseline and real consumption are not
simultaneous in time; baseline measurements constitute the energy consumption pattern of a data centre before
implementing actions or changes in SW or equipment that imply variations in this pattern (flexibility). Thus, it is
necessary to normalise the resulted curves in order to compare the profile of energy consumption without
introducing this distortion. The equation below provides the mathematical formula to compute this factor:
(8)
where:
 denotes the data centre’s real energy consumption in kWh at a given point in time, i. This is the
energy consumption after the implementation of flexibility mechanisms has taken place.
 denotes the data centre’s baseline energy consumption in kWh at a given point in time, i. This
is the energy consumption before the implementation of flexibility mechanisms. Baseline energy
consumption profiles must be obtained by analysing and modelling the energy consumption of the data
centre before the actions provided by flexibility mechanisms have been implemented (see section 3.3.2.2 for
details).
 represents the sample size.
The numerator within the formula for DCA represents the cumulative, absolute error between and
, while the denominator represents the summation of over the whole sample, in other words the
area below the baseline curve.
A DCA value equal to 1 means that the curve has not changed. A high flexibility of the consumption curve will imply a
lower value for DCA (very different curves can cause even DCA negative values).
3.3.2 Measurements
According to the formula aforementioned, the only parameter to be measured is the total energy consumption of
the data centre at a given point in time in kWh. More information on the selected timeframe and the baseline
scenario is found within sections 3.3.2.2 and 3.3.2.3. For details on measurement points please refer to section
3.3.2.4.
The baseline is evaluated experimentally measuring power values at points A and B of the diagram as well as the
Point of Delivery (POD) of the DC. Given that the DC can be considered as a “black box”, no other measurements are
needed in this case. To perform the baseline evaluation an appropriate value for Δt must be selected.
We are dealing here with a metric in which the variation as a function of time is of particular importance. Typical
daily profiles must therefore be measured. To do so, frequent measures of Energy consumed must be taken to

35
determine the baseline scenario. Obviously, the same frequency will be adopted to obtain the measures in the “real”
scenario, i. e. after actions/changes in SW or equipment that imply variations have taken place. For practical
purposes Δt = 15 min seems adequate (choice currently being used in DC4Cities).
Daily profiles must be collected under different operational conditions of the DC. Effectively, profiles can exhibit
significant variations depending on seasonal effects (winter/summer), workload changes (i.e. working day/weekend,
specific deadlines, etc.). In order to assess correctly the Baseline it will be therefore necessary to create a set of
typical profiles. Requisites and characteristics of the set must be determined for the specific DC under study.
For this metric Baseline adjustment is not needed, due to the usage of the scaling factor .
Figure 6 illustrates a typical scenario of a data centre. The total energy consumption of such a data centre is
measured at the Point of Delivery (POD) and it is the summation of energy coming from the grid (A) plus energy
generated onsite (B); both measurements are in kWh. This means that all sources of energy are considered and
converted in electricity. For details on conversion factors please refer to the cluster report pertaining to the metric
“Primary Energy Savings”.
Figure 6: Data Centre Control Volume and Measurement Points

36
database. A properly sized storage system must be designed and installed and data must be available for the
next phase of analysis and verification. In this way, each meter, becomes a node of the network.
Finally, measurement errors (defined by the HW’s accuracy classes), connections, communication protocols and
networking must be compliant with the existing standards (ANSI, IEC, IEEE, CEI EN) and national regulation. The
requirements specified in these standards and regulations have to be considered as minimum values for the
meters under normal working conditions. For special application, higher constraints might be necessary and
should be agreed between the user and the manufacturer.
the sampling time.
- Every time the gateway does not communicate with the storage system or one or more nodes do not
- Periodically (once a year) it is important to inspect the measurement equipment and repeat the calibration
procedure.
- Every time hardware changes occur, the compliance between the meter range and the physical variable
If the commissioning procedure is workmanlike performed a maintenance procedure is not indispensable.
As DCA is based on comparing the total energy consumption of a DC to its energy consumption during a previous
time period, the main assumptions will be that Sections 3.3.2.1, 3.3.2.4 and 3.3.2.5 provide all the necessary
information (metering equipment, location) to measure the total energy consumption of the DC. All meters are
assumed to be calibrated, commissioned and tested, so the energy consumption measurement values are accurate
and rigorously collected and the samples are representative. If these assumptions are not met, an analysis to detect
which measurements are being missed or are not being considered should be made, and a procedure to include
them with new equipment or with estimations must be applied, including quantifying all the uncertainties added in
case of estimating any consumptions.
For Section 3.3.2.2, the assumption will be that all the different baselines and profiles necessary for measurement
and verification process are defined. As these baselines will be used to calculate the energy savings after the energy
saving process it is important that the consumption behaviour of the DC is completely addressed.

37
data storage limitations, SCADA or Software limitations etc.), to capture the energy consumption pattern a
minutes.
of the daily energy pattern consumption of a DC, being adequate for the creation of different energy baselines.
and all different weather conditions: for this reason, a whole year duration is recommended. Similarly, variable DC
specific metering duration periods will be selected, depending on usage and location of the DC, in order to give a
fair representation of the DC behaviour.
3.3.3 Example
Indicatively, Figure 7: Energy consumption, both real and baseline presents an example calculation of the DCA
flexibility metric for a hypothetical DC. Specifically, assuming that the measurement procedures described in the
previous paragraphs were respected, Figure 7: Energy consumption, both real and baseline depicts the data centre’s
real energy consumption in kWh and the data centre’s baseline energy consumption in kWh
for 7 consecutive time intervals, i = 1,..,7.
In the example assumed, the adjustment factor equals . Therefore, DCA can
be calculated as follows:
(9)

38
Figure 7: Energy consumption, both real and baseline
3.4 Primary Energy Savings
This section discusses in detail the metric denoting the savings in terms of primary energy consumed by a data
centre, once improvements have taken place with regard to its energetic, economic, or environmental management.
This metric was initially introduced within the report released by the cluster as a result of the work on identifying
new metrics to accommodate for newly introduced dimensions such as usage of renewable energy sources, energy
re-use, and data centre flexibility mechanisms9
. The suggested formula was given as:
(10)
The current section, taking as a starting point the above formula, will further elaborate and detail the proposed
metric along with guidelines pertaining to its computation and related measurements.
For all intents and purposes of this section definitions pertaining to commonly used terms are those specified within
ISO/IEC JTC 1/SC39 documents, unless otherwise explicitly stated, and in particular (ISO/IEC JTC 1/SC 39, 2014).
To better understand what this formula represents, one may collapse the parameters involved as follows while
introducing the percentage aspect:
9
https://ec.europa.eu/digital-agenda/en/news/cluster-fp7-projects-proposes-new-environmental-efficiency-metrics-data-
centres
20
22
29
33
35
32
26
25
30
35
30
35
30
25
0
5
10
15
20
25
30
35
40
0 1 2 3 4 5 6 7 8
energyconsumption(MWh)
time i
EDC Real EDC Baseline

39
(11)
where,
denotes the total energy consumed by the data centre, in terms of primary energy, measured during
period as a summation of discrete intervals10
in time, while
denotes the total energy that would have been consumed by the data centre, also in terms of
primary energy, during the “same” period provided that conditions would have remained the same as in the
baseline scenario.
Total energy in this case takes into consideration not only the energy consumed by the data centre in form of
electricity, , but also energy consumed in any other form, , such as for example chilled water for
cooling purposes:
(12)
All parameters are in primary energy terms with the unit of measurement being the kWh.
The selected period may depend on the type of the data centre in question, the characteristics of its business
model, the type of the intervention and so forth. On a business as usual scenario, one should opt for a period equal
to a year – so as to take into account a statistical average of the typical behaviour of a data centre, including end-
user’s consumption patterns, along with variations pertaining to environmental conditions, such as outside
temperature, sun and wind intensity. However, for practical reasons and for the purposes of the projects within the
cluster this period may be adapted to fit the general planning.
For details on the selection of the timeframe along with information on the baseline scenario please refer to
sections 3.4.2.2 and 3.4.2.3.
It should be noted that this metric along with the related ones on CO2 emissions and economic expenses are meant
to be global metrics11
; as such the data centre is therefore viewed as a black box. Both of these metrics are actually
variants of the one pertaining to the primary energy, adjusted with appropriate factors.
More information on the related metrics and their measurement and validation plan is found within their respective
reports.
In the following we provide details on the methodology related to measurement, analysis, and reporting of this
metric.
3.4.2 Measurements
Based on the formula aforementioned and in line with the concept of treating the data centre as a black box the
parameters to be measured are:
10
Discrete interval will depend on the goal pursued to compute the metric, and may vary from a time interval equal to 15
minutes or 1 hour to a time interval of 1 week, month or year.
11
In case the data centre operator is interested in evaluating improvements pertaining to a subsystem instead (such as primary
energy savings of the cooling system), the methodology defined within this section may be applied; naturally the measurement
boundaries would have to be adjusted accordingly.

Smart City Cluster Collaboration, Task 4. Measurement and Verification Methodology for Energy, Environmental and Economic Efficiency Metrics.

Smart City Cluster Collaboration, Task 4. Measurement and Verification Methodology for Energy, Environmental and Economic Efficiency Metrics.

Empfohlen

Empfohlen

Weitere ähnliche Inhalte

Ähnlich wie Smart City Cluster Collaboration, Task 4. Measurement and Verification Methodology for Energy, Environmental and Economic Efficiency Metrics.

Ähnlich wie Smart City Cluster Collaboration, Task 4. Measurement and Verification Methodology for Energy, Environmental and Economic Efficiency Metrics. (20)

Mehr von DC4Cities Project: Environmentally Sustainable Data Centre for Smart Cities

Mehr von DC4Cities Project: Environmentally Sustainable Data Centre for Smart Cities (8)

Kürzlich hochgeladen

Kürzlich hochgeladen (20)

Smart City Cluster Collaboration, Task 4. Measurement and Verification Methodology for Energy, Environmental and Economic Efficiency Metrics.