Analysis of the Electricity Consumptions: a First Step To

Sustainable Cities and Society 23 (2016) 23–36

Contents lists available at ScienceDirect

Sustainable Cities and Society

jou rnal homepage: www.elsevier.com/locate/scs

Analysis of the electricity consumptions: A ﬁrst step to develop a district cooling system

a a a b,∗

Luca Pampuri , Nerio Cereghetti , Davide Strepparava , Paola Caputo

Istituto di Sostenibilità Applicata All’ambiente Costruito, Scuola Universitaria Professionale della Svizzera Italiana, Campus Trevano, Via Trevano,

CH-6952 Canobbio, Switzerland

Architecture, Built Environment and Construction Engineering Department, Politecnico di Milano, Via Bonardi 9, 20133 Milano, Italy

a r t i c l e i n f o a b s t r a c t

Article history: Space cooling represents an increasingly and often understated important energy demand even in moder-

Received 23 December 2015

ate climates. Usually space cooling is provided at building level by electric driven appliances. This implies

Received in revised form 17 February 2016

several problems in summer due to the peak of electricity consumption.

Accepted 18 February 2016

Analytical methods for investigating energy consumption due to space cooling demand at urban or

Available online 26 February 2016

regional level are needed. The research here reported is focused on the territory of southern Canton

Ticino, in Switzerland, and is based on real data provided by the local electricity company. The research

Keywords:

investigates the electricity consumptions of big users in order to verify if there is a signiﬁcant cooling

District cooling

demand, how this demand affects electricity consumptions and if this demand can be satisﬁed by district

Electricity consumption

cooling (DC). The possible DC connections were selected by a deﬁned procedure and mapped by GIS, as

Energy consumers

Clustering well as the density of the electricity consumption and the peak power. Three main areas suitable for DC

Energy signature were identiﬁed. The analysis demonstrates that DC could represent an alternative to electric driven air

GIS conditioning systems, with beneﬁts for the consumers, the utilities and the environment.

1. Introduction Despite the energy efﬁciency measures developed in Europe and

in Switzerland, the global energy consumption in buildings con-

The path to carbon neutral communities represent long lasting tinues to rise. In particular, considering the focus of the present

and urgent challenges for all governments. For example, the EU has paper, BFE (2015) stated that energy consumption related to air

approved the “Roadmap for moving to a competitive low carbon conditioning had increased of 39% from 2000 to 2013. Many author-

economy in 2050” (EU COM112/2011, 2011) with the objective to itative researches have investigated space cooling in terms of

reduce greenhouse gas emissions by 80–95% by 2050 in compar- predicting cooling loads in buildings, but a signiﬁcant level of

ison to those of 1990. Analogously Switzerland has approved the complexity is recognized in this framework as reported also in

Energy Strategy 2050 that includes the main aims for the energy Damnu, Wanga, Zhai, and Li (2013). The present research sug-

policy towards 2050 (BFE, 2015). Among these aims, as conse- gests another approach, that starts from the available electricity

quences of abandoning nuclear power, a reduction of the electricity consumption, since space cooling demand is usually provided by

consumption is required. compression chillers. The increasing of the electricity demand due

Energy statistics show that European and Swiss buildings are to space cooling is in contradiction with the deﬁned energy targets,

responsible of more than the 40% of the total ﬁnal energy con- as empathized also by the media during the hot summer of 2015

sumption (Odyssee, 2012; BFE, 2015), representing an important (IEA, 2015).

energy saving opportunity. More in details, energy statistics show In general, it is difﬁcult to obtain reliable data about electric-

that buildings space heating accounts for 68% and 71% of end-use ity consumption for a large number of users (Caputo, Costa, &

energy consumption respectively in Europe and Switzerland, while Zanotto, 2013). Furthermore, with few exceptions, these data are

lighting and electrical appliances account for 15%, water heating for available as a whole, without the details of electricity consump-

12% and cooking for 4% (Odyssee, 2012; BFE, 2015). tion for cooling demand. The evaluation of the summer surplus due

to space cooling is difﬁcult because often data do not cover a full

year and signiﬁcant sample of users. For these reasons, a methodol-

∗ ogy for analyzing this hidden energy demand and for studying how

Corresponding author. Tel.: +39 0223999488; fax: +39 0223999484.

E-mail address: [email protected] (P. Caputo). it can be satisﬁed without affecting electricity consumptions was

http://dx.doi.org/10.1016/j.scs.2016.02.015

24 L. Pampuri et al. / Sustainable Cities and Society 23 (2016) 23–36

Fig. 1. Territorial context of the study (left) and map of municipalities supplied by AIL (in red) in Canton Ticino (right). (For interpretation of the references to color in this

ﬁgure legend, the reader is referred to the web version of this article.)

•

developed. The present paper reports the results of a research con- conventional compressor-based cooling;

•

ducted on the territory of southern Canton Ticino, in Switzerland absorption cooling, transforming waste heat into cooling;

•

(Fig. 1). Even though this territory is small, it can be considered rep- free cooling, using cool ambient air or cool water from the ocean,

resentative of a large arear with similar climatic and morphological lake or river.

conditions. The research starts from the analysis of real data pro-

vided by the local electricity company (AIL SA, 2015) that supplies

Euroheat and Power (2006) afﬁrms that a DC system can reach

electricity to most of southern Canton Ticino (Fig. 1). Following the

efﬁciencies typically 5 or even 10 times higher than the efﬁciencies

methodology reported in Section 3, the research investigates the

of electric chillers, as it is shown also in Table 1, where primary

electricity consumptions of big users in order to verify how space

resource factors (PRF) are adopted. Primary energy refers to energy

cooling affects electricity consumptions and if it can be satisﬁed

that has not been subjected to any conversion or transformation

by district cooling (DC). The analysis demonstrates that, despite

process (e.g. oil in the oil ﬁelds). Primary energy may be resource

the temperate climatic conditions, there is a signiﬁcant consump-

energy or renewable energy or a combination of both. Resource

tion of electricity due to the space cooling demand, especially in

refers to a source depleted by extraction (e.g. fossil fuels) and

the tertiary sector and that DC can be an efﬁcient alternative to

renewable energy to a source that is not depleted by extraction

conventional cooling devices.

(e.g. biomass, solar). The use of the primary resource factor (PRF)

enables to measure the savings and losses occurring from energy

generation to the delivery to the building. The primary resource

2. District cooling as an alternative to conventional cooling

devices factor expresses the ratio of the non-regenerative resource energy

required for the building to the ﬁnal energy supplied to the build-

ing. The primary resource factor represents the energy delivery but

El Barky and Dyrelund (2013) report an interesting summary

excludes the renewable energy component of primary energy.

about the advantages of moving from conventional space cooling

Since southern Canton Ticino has several natural sources suit-

devices, chillers operating at building level, to DC. The operation of

able for DC such as the Lake of Lugano and several local rivers, free

electric chillers implies large investments in power peak plants and

cooling (i.e. the adoption of cold water for cooling purposes) could

even for blackouts in many cities around the world. A light reduc-

be the most appropriate technology in this case.

tion of the peak demand can be achieved in high thermal capacity

buildings, by other measures and users behavior, not always appli-

cable. 2.1. District cooling potential in Europe and Switzerland

In case of proper morphologic, climatic and density features of

the built environment, DC can be adopted as an alternative to con- DC still represents an uncommon technology for providing cool-

ventional cooling devices. As well explained by El Barky (2013), DC ing. For example, the current DC market share represents only 1–2%

can bring advantages similar to those of district heating (DH) due of the cooling market in Europe and DC is almost not adopted in

to centralized production (i.e. risk reduction for the individual con- Switzerland. Despite of this, there are interesting examples in Asian

sumers and cost effectiveness for the energy supply companies). DC and European cities (IEA, 2015; Xiang-li, Lin, & Hai-wen, 2010), i.e.

can contribute to use local sources that otherwise would be wasted. Stockholm, Helsinki etc.

Euroheat and Power (2006) reports that DC is normally pro- Euroheat and Power (2006) estimated that the European cool-

duced by means of one or more of the following technologies that ing market would be around 660 TWh/y in the period 2012 to

can be also combined: 2020. This corresponds to an electricity consumption of 260 TWh,

L. Pampuri et al. / Sustainable Cities and Society 23 (2016) 23–36 25

Table 1

Performance of different cooling solutions (Euroheat and Power, 2006).

Solutions EER PRF

Conventional room and central air conditioning 1.5–3.5 1.7–0.7

District cooling solutions

Industrial chillers with efﬁcient condenser cooling and/or recovered heat to district heating 5–8 0.5–0.3

Free cooling/industrial chillers 8–25 0.3–0.1

Free cooling and cooling spills 25–40 0.1–0.06

Absorption chiller driven from heat from waste or renewable sources 20–35 0.13–0.07

EER: (Seasonal System) Energy Efﬁciency Ratio. This states the output of yearly exploitable cooling energy per unit of yearly electrical energy input in the system.

PRF: Primary Resource Factor.

considering a seasonal system energy efﬁciency ratio of 2.5. There- On the other hand, the impact of DC on local infrastructures and

fore, according to Eurostat (2015), it is possible to summarize that ﬁnance is very important. DC requires signiﬁcant investments, a

electricity consumed due to cooling needs represents about the stable policy framework, and collaboration between various stake-

8% of the yearly electricity generation in EU. These data demon- holders. This means that DC should be promoted in the framework

strate the large potential of penetration for DC in Europe and in of appropriate conditions. In this case, DC can become a very impor-

Switzerland, a small country in the European Alpine region. Since tant tool toward improving energy efﬁciency, reducing greenhouse

average temperatures in Switzerland are likely to increase due to gases emissions and renewable energies integration (Bjerregaard,

climate change, it is expected that energy demand for space heating 2013).

will decrease while space cooling, which is currently almost non-

existent in private households in Switzerland, is likely to increase.

3. Methodology

As recommended by Christenson, Manz, and Gyalistras (2006) and

in CH2014-Impacts (2014), this phenomenon should not be under-

In the previous sections, it is stated that the cooling demand is

estimated. This last study reports that Swiss heating degree days

constantly increasing and that it strongly affects the consumption

(HDD) are going to decrease from 5% to 20% in 2050 in compari-

of electricity. Moreover, it is stated that electricity consumption

son to the reference period (1980–2009). Conversely, in the same

due to cooling deserves to be deeply analyzed. This paper reports

study it is projected that Swiss cooling degree days (CDD) are

a method for exploring cooling demand of big electricity users at

going to increase from 100% to 400% in 2050 in comparison to the

regional scale, southern Canton Ticino, and focuses on DC potential

same reference period. Analogous considerations are reported in

in the same region.

the framework of other interesting researches in Europe (Taseska,

The case study is very interesting because it originates from

Markovska, & Callaway, 2012; Berger et al., 2014).

a research supported by the local electricity company (AIL SA,

For these reasons studies about how the increasing cooling

2015) that supplies electricity to most of southern Canton Ticino,

demand can be satisﬁed reducing its impact on the energy systems

as reported in Section 1 and Fig. 1. This case attests the inter-

are needed.

est of energy utilities in DC underlining the innovation and the

replicability of the methodology. Furthermore, it represents a pre-

cious occasion for managing real data and for foreseeing potential

2.2. Main beneﬁts of district cooling

synergies among DC, DH and distribute generation. To develop a

hypothetical DC project, it is fundamental to map the potential

The phenomenon of electric summer peak due to cooling is

customers in a certain area. Therefore, since the beginning of the

stressed also by Jing, Jiang, Wu, Tang, and Hua (2014), underlining

analysis, the chosen methodology includes an important use of GIS.

the high operating costs.

GIS supported tools are very attractive for energy utilities because

Interesting evaluations about beneﬁts related to DC as an alter-

allow a quick overview of the potential operations in a deﬁned

native to building size electric driven air conditioning system are

territory.

reported in Bjerregaard (2013), De Carli, Galgaro, Pasqualetto, and

Zarrella (2014) and Gang, Wang, Gao, and Xiao (2015).

3.1. Datasets and selection of the users

As main beneﬁt, DC can contribute to smooth the demand curve

of electricity, especially if free cooling, such as rivers and lakes, or

AIL SA provided a geo-referred dataset of the electricity con-

surplus heat through absorption chillers were adopted.

sumptions among all the users of the served territory. As big

El Barky (2013) reports an interesting summary of beneﬁts of

electricity users, only consumers with more than 100 MWh/y were

DC on users, utility companies and communities as follows:

taken into account (AIL, 2014a). In general, they are tertiary or

industrial users. Additionally, other two available databases were

•

beneﬁts for users: cost reduction (in particular in case of free considered: the federal register of buildings (RBD, 2014) and the

cooling or use of waste heat), no noise from chillers, reduced risk list of the users connected to the water grid for industrial uses,

reparations, better certiﬁcation of the energy performance of the provided in AIL (2014b).

building and available space on rooftop and basement for other AIL (2014a) reports the list of users with a yearly electricity

purposes; consumption bigger than 100 MWh, with the address of the users.

•

beneﬁts for utility companies: a new proﬁtable market, a new The electric consumptions are recorded every 15 min. The data

product and service in portfolio, synergies between power, heat collected demonstrate that there are 957 users with an electric

and cooling production, especially in case of distributed genera- consumption over 100 MWh/y. This corresponds to a total con-

tion; sumption of 487,500 MWh/y.

•

beneﬁts for communities: environmental and energy improve- In the RBD (2014) are listed the most important data for describ-

ments related to a more efﬁcient use of the resources, possibility ing buildings and dwellings in addition to their address.

of creating synergies in the energy and infrastructural planning AIL (2014b) reports data about water taken from the lake

of their territory. of Lugano and supplied for industrial uses. Approximately

26 L. Pampuri et al. / Sustainable Cities and Society 23 (2016) 23–36

6 million m per year of water are extracted at a temperature of temperature drastically decrease in winter season (electric driven

◦

about 7–9 C. The data include technical information about the heating system).

users (addresses, type of uses, etc.). In the analyzed case study, the energy signatures were provided

The ﬁrst step of the research regarded the identiﬁcation of users as a graph representing the daily mean power in function of the

that surely present a signiﬁcant cooling demand and their location, daily mean temperature. At the end of this phase of analysis, the

in order to verify the feasibility of a DC system. following typologies of users were founded:

Data were elaborated in order to carry out a classiﬁcation of the

•

users based on their activities (mainly of the tertiary sector). users without heating and cooling demands provided by elec-

First, some big users were excluded from the list of hypothetical tric driven systems. In this case, electricity consumptions are

cooling consumers because of their activity (if the activity of the meanly uniform throughout the year. These users were obviously

user implies a negligible cooling demand) or because of inconsis- excluded from the further phases of the analysis;

•

tencies or lacks encountered during the data elaboration. users with cooling demand provided by electric driven systems.

In particular, the users related to the following activities (AIL, In this case, electricity consumptions increase during the summer

2014a), were excluded: aqueducts, temporary connections, breed- season;

•

ing and stables, gas cabinets, construction sites, churches and users with heating and cooling demand provided by electric

convents, docks, private garages, plants for water treatment, incin- driven systems. In this case, electricity consumptions increase

erators, landﬁlls and public lighting. during the winter and the summer season.

Second, the users connected to the water grid for industrial

uses were excluded from the list of hypothetical cooling consumers

The last two typologies, exempliﬁed in Fig. 4, were considered

because it is conceivable to assume that they already satisfy their

in the further steps of the research.

cooling demand using industrial water, since this service is cost

effective.

3.3. Map of the electricity consumers with cooling demands

Electricity data of the users that pass the selection were then

elaborated. In particular, in order to investigate cooling demands,

At the end of the data elaboration process described in Sections

electricity data were analyzed referring to outside temperature.

3.1 and 3.2, it was possible to deﬁne the ﬁnal list of users with

The period from April to September was considered because in

space cooling demand and to provide a map of their location by

these months the heating demand in Ticino is negligible, so, even

GIS. This is reported in Fig. 5, where the green dots represent users

if there were electric driven heating systems, the electricity con-

with cooling demand, the red dots represent users without cooling

sumption would not be affected by heating. Hypothetical space

demand and the yellow dots represent users that have to be further

cooling users should have electric consumptions that increase pro-

investigated. This methodological step is fundament in planning DC

portionally when the outdoor temperature rises. Therefore, users

systems as conﬁrmed also by the technical literature. For example,

with electric consumptions not depending on outside temperature

Euroheat and Power (2006) stated that in order to identify the most

were excluded from the analysis, as explained in Section 3.2.

optimal location of a new DC plant within a certain area, it can be

Finally, through the support of GIS, the farthest users (far more

valuable to use an automated mapping tool. The estimated cooling

than 500 m from other four potential connections) were excluded

demand and geographical location of each building in a deﬁned area

from the analysis because their connection to a DC system is not

permit to calculate also the economic difference between a con-

feasible due to the long distance from the hypothetical grid, as

ventional cooling system and a DC system. This operation includes

explained in Section 3.3.

a classiﬁcation of building size, cooling demand and building type.

The process of the selection is shown in Fig. 2.

Moreover, a careful consideration of the local natural resources can

really increase both proﬁtability and energy efﬁciency.

The GIS map of the locations of the potential cooling user in

3.2. Energy accountability and external temperature

the analyzed territory (Fig. 5) permits also to deﬁne a geographic

criterion for the selection of users suitable for DC systems. This cri-

A precious help in deﬁning how electricity consumptions are

terion serves to detect a minimum space cooling density in order

affected by cooling needs is represented by energy accountability.

to deﬁne where the DC grid is technically and economically feasi-

In Switzerland, energy accountability is considered as an impor-

ble. Considering the characteristics of the climate and of the built

tant tool for analyzing, monitoring and controlling the energy

environment, the following criterion was adopted: each user can be

demands in buildings and the related economic impacts. The main

considered as a hypothetical connection of the DC system if there

purpose of energy accountability is the continuous monitoring

are at least other four users within a distance of 500 m as reported

of energy ﬂuxes and related costs. This approach also permits to

in Fig. 6, with grey buffers. This preliminary criterion can be mod-

plan energy retroﬁt interventions and other energy management

iﬁed or improved in the following phases of developments of the

improvements. Energy accountability includes also interesting rep-

research, as reported in Section 4.

resentations of data by schemes and graphs. Among these, there is

the useful representation of the energy signature (Zhao & Magoulès,

3.4. Deﬁnition of cooling proﬁles

2012), a graphs that reports on the abscissas the outdoor weekly

mean temperatures and on the ordinates the weekly energy con-

At the end of the data elaboration described in Sections 3.1, 3.2

sumptions (heat or power).

and 3.3, it was possible to edit the ﬁnal map of users for which space

The energy signature of a building can be compared with deﬁned

cooling is supposedly provided by electric driven systems. The fol-

typical curves, as reported in Fig. 3.

lowing step of the research regards the deﬁnition of the “cooling

In particular, type 1 means that energy consumptions are not

proﬁle” (CP) of each user, including the calculation of the elec-

affected by the trend of the outdoor temperature; type 2 indi-

tricity consumed and of the maximum power due to space cooling

cates that energy consumptions linearly increase with the outdoor

demand.

temperature during all the year; type 3 indicates that energy con-

sumptions linearly increase with the outdoor temperature only in

summer season (electric driven cooling system) and type 4 indi-

cates that energy consumptions linearly increase when the outdoor The proﬁle of the electricity adopted for space cooling.

L. Pampuri et al. / Sustainable Cities and Society 23 (2016) 23–36 27

Selecon of suitable

Sele con of users with Eliminaon of users

Data cleaning users depending on

remo te metering without energy data

their typology

Eli minaon of users

Calculaon and GIS map whose energy Elimin aon of users

GIS ma p of suitable

of cooling energy and consumpon does not co nnected to industrial

users

power depe nd on outdoor water net

temp erature

Calculaon and GIS map

of possible DC areas

Fig. 2. Scheme of the selection of the electricity consumers potentially interested in DC.

Fig. 3. Typologies of energy signatures.

First, for each user the electricity data provided by AIL (2014a) day). The basic idea of this step is to separate days in sets related

were analyzed taking into account the period from April to to cooling demand and others without this contribution.

September 2013. As explained in Section 3.1, during this period Data clustering is performed using K-means algorithm (Arthur

in Ticino heating demand is negligible, so the clustering process, & Vassilvitskii, 2007). This algorithm converges to a good solution

described in the following, is not affected by consumptions related (not necessarily the optimum) given a number of clusters (k). The

to heating. Conversely in the same period it is possible to have days algorithm output is constituted by k clusters, each of them deﬁned

with or without cooling demand. by the belonging days and a centroid as mean reference. In this case,

The methodology for cooling proﬁles (CPs) calculation of a single a centroid is a 24-h proﬁle with 96 values. The generic centroid’s

user is constituted by the sequence of the following steps: element i is the mean of the corresponding i values related to the

cluster’s days.

•

Data clustering: electricity consumption data are analyzed to Therefore to the fact that k is an input, the followed strategy

obtain meaningful sets of days; was to develop an algorithm that performs K-means more times

•

Clusters grouping: clusters obtained in the step 1 are grouped with k higher and higher and then to choose the best solution. To

considering their distribution over the days of the week and the implement this iterative approach a MATLAB script (scout) was

period; developed. The following table reports the input parameters of the

•

CPs calculation: the cooling proﬁles are calculated using the clus- script.

ters groups deﬁned in the previous step. Scout permits to perform K-means for (maxNumClusters-

minNumClusters) times, with k starting from minNumClusters until

3.4.1. Data clustering maxNumClusters, discard solutions with clusters related to popula-

For each user the electricity data in the period April–September tions smaller than minPopCluster and choose the best result in the

(182 days) are analyzed with a resolution of 15 min (96 values per remaining subset. The last choice is actuated considering as best

28 L. Pampuri et al. / Sustainable Cities and Society 23 (2016) 23–36

Fig. 4. Examples of energy signatures that depend on outdoor temperature: electricity for cooling and heating (above); electricity for cooling (below).

•

solution the one minimizing the standard deviations’ sum related G1, composed by centroids 2, 4 and 6. It shows a schedule of

to clustered data. operation from 8 a.m. to 7 p.m. step up approximately;

•

Each K-means running is performed with a replication equal to G2, constituted by centroids 3 and 5. It shows a schedule of oper-

50 to avoid the risk of local minima. As said before, the cluster- ation from 8 a.m. to 10 p.m. approximately;

•

ing algorithm can converge to a local minimum, depending on the G3, with centroid 1. It shows a schedule of operation is stable

initial conditions. The algorithm is launched more times by replica- approximately constant all over the day.

tions and the optimum solution can then be obtained reasonably.

Fig. 7 shows the centroids found by scout algorithm for a generic Then the distribution of clusters elements is analyzed taking

user. In this case the solution is represented by six clusters. into account the days of the week (Fig. 8) and the daily average of

Lugano’s temperature during the period April–September (Fig. 9).

3.4.2. Clusters grouping Fig. 8 shows how G2 is strongly related to Thursdays, G3 to

Once performed the clustering and calculated the centroids, the Sundays and G1 to the other cases. Therefore, it is reasonable to

second step to obtain CPs is to ﬁnd associations between clusters. establish two types of CP, one relating to the clusters G1 and another

Considering Fig. 8 is reasonable to divide them into the following concerning the clusters G2. No CP is considered for G3, being con-

three groups: stituted only by a centroid.

L. Pampuri et al. / Sustainable Cities and Society 23 (2016) 23–36 29

Fig. 5. Location of the potential DC users (green dots: users with cooling demand; red dots: users without cooling demand; yellow dots: users which have to be further

investigated). (For interpretation of the references to color in this ﬁgure legend, the reader is referred to the web version of this article.)

Fig. 6. Analysis of the density of the cooling demand in relation to a buffers of 500 (grey circles around each user).

30 L. Pampuri et al. / Sustainable Cities and Society 23 (2016) 23–36

Fig. 7. Centroids related to a clustering solution for a single use.

•

On the other hand, Fig. 9 shows that G1 centroids are distributed The complete automation of CPs calculation has to be imple-

in cool (cluster 6), warm (cluster 2) and hot (cluster 4) days. A sim- mented, reasonably focusing the methodology on a smaller set

ilar result regards G2 centroids, with cluster 5 related to warm/hot of users and without considering their geographical distribution.

days and cluster 3 to the cool ones. Actually the methodologies explained in Section 3.4.1 and 3.4.3

are automatic, but not clusters grouping (Section 3.4.2);

•

Once the previous automation is complete, an uncertainty anal-

3.4.3. Cooling proﬁles calculation

ysis has to be actuated for errors estimation.

The third step is to calculate the cooling proﬁles as the difference

between centroids related to hot/warm and cool days, considering

the weekdays grouping described in Section 3.4.2. The following

4. Results

equations report the CPs deﬁnitions for the solution shown in Fig. 7.

Section 3 describes the procedure aimed at deﬁning the electric-

CP1 (t) = Centroid4 (t) − Centroid6 (t) (1)

ity consumption and peak power due to space cooling requirement

CP2 (t) = Centroid2 (t) − Centroid6 (t) (2) of big users in the territory of Canton Ticino served by AIL SA.

This important result has to be integrated by other elaborations

t =

CP3 Centroid5 t − Centroid3 t (3)

( ) ( ) ( ) necessary to study the feasibility of a DC grid. Powers and ener-

gies described in Section 3 were represented by a GIS map; then

CP1 is obtained by clusters 4 and 6 and is related to hot days

their density was calculated as energy and power per surface unit,

of the week except Thursdays and Saturdays. CP2 is similar, but

considering a grid of one hectare. This elaboration permits to have

related to warm days. Instead, the third cooling proﬁle is associated

an idea of the area in which DC could actually be a promising and to hot Thursdays.

effective technology.

It is important to consider that clusters 6 and 3 can be seen as

The authors have veriﬁed the technical literature in order to ﬁnd

the minimum bands of power consumption, related to all types of

benchmarks useful in this phase of the analysis, but they became

demand except cooling (i.e. lighting and appliances). Indeed these

aware that the data founded were not directly usable. The consulted

two clusters have been obtained considering days with negligible

references are mainly related to analogous criteria for planning

cooling demand (Fig. 9).

DH grids (Aste, Buzzetti, & Caputo, 2015; Brum, Erickson, Jenkins,

For each CP the daily electric consumption for cooling purposes

& Kornbluth, 2015) that are more common than DC grids, espe-

(Ecool) and the related peak power (Pmax) are calculated with the

cially in Europe. Based on an authoritative study provided by the

following equations:

⎡ ⎤ Swiss Federal Ofﬁce for Energy (Oppermann, Gutzwiller, & Müller,

n 96 2010), a space heating density of 30,000 MWh/y per square kilo-

E = ⎣P × t t⎦ meter is considered as standard value for developing DH grids in

cool i CPi ( ) d (4)

the context of Canton Ticino. On the contrary, it is difﬁcult to deﬁne i=

1 t=1

analogous standards for DC. The technical literature related to DC

P is mainly focused on networks design (Xiang-li et al., 2010), rather

max = max (CPi (t)) (5)

than on standards related to space cooling density and size of the

where Pi is the number of hot/warm days related to cooling global cooling needs. As an example, Euroheat and Power (2006)

proﬁle i. reports that statistically district cooling has been introduced down

For example, considering CP1 described by Eq. (1), P1 is the num- to 0.5 kW per meter of grid of distribution, i.e. in larger and middle

ber of days belonging to cluster 4. size cities, within expansion areas such as retail and business parks,

It is important to remark that this paper shows a ﬁrst approach hospitals, airports, etc.

to the cooling proﬁles estimation. Especially the next two steps The authors have stated from the beginning that this paper

should follow this study to have an exhaustive analysis: focuses on the ﬁrst steps required to develop a DC system, starting

L. Pampuri et al. / Sustainable Cities and Society 23 (2016) 23–36 31

Fig. 8. Clusters grouped by days of the week.

Table 2

from the analysis of the electricity consumptions. The transition

Input parameters of scout MATLAB script.

from the electricity consumptions for space cooling to the cool-

ing demands implies the knowledge of the cooling systems and Parameter Description Meaningful values

of the equipment installed into the buildings. Furthermore, the

minNumClusters Minimum number of clusters 2

transition from the cooling demands to the cooling power for grid maxNumClusters Maximum number of clusters 6–8

length unit implies, at least, a preliminary design of the layout of minPopCluster Minimum population of a 10–15 days

cluster

the DC grid. Since these activities will regard the following devel-

Replications Number of K-means 50

opments of this study, it was not possible to take into account

replications to avoid local

at this stage of the research the benchmarks previously men- minima

tioned.

At the end of the procedure described in Section 3, only 238

of the 957 big electricity consumers originally provided by AIL electric driven cooling users. The table shows the predominance

SA were selected as users with a space cooling demand suitable of the tertiary sector. The calculated total electricity consump-

for DC as reported in Table 2. This result conﬁrms that an accu- tion and peak power are 9,070 MWh/y and 10.4 MW, respectively,

rate analysis is needed in order to select correctly the potential which conﬁrms a great potentiality for DC. In fact, at least for the

Fig. 9. Clusters grouped by different types of external temperature.

32 L. Pampuri et al. / Sustainable Cities and Society 23 (2016) 23–36

Fig. 10. Representation of the density of electricity consumptions for space cooling (red dots represent the 238 selected cooling users). (For interpretation of the references

to color in this ﬁgure legend, the reader is referred to the web version of this article.)

238 selected consumers, the electric consumption for space cool- needed for space cooling in kW per hectare. The main data of the

ing purposes represents respectively the 7% of the total electricity potential DC users are reported in Table 3.

and the 9% of the total electricity during the day hours (exclud-

ing night hours). Although information about cooling systems and

equipment installed into the buildings are not available at this stage 4.1. Identiﬁcation of three areas suitable for DC

of the research, a typical value of seasonal efﬁciency of the electric

driven air conditioning systems (i.e. equal to 3) was adopted . By Information provided by Figs. 10 and 11 constitutes an impor-

this mean seasonal coefﬁcient of performance a cooling demand of tant basis to deﬁne areas suitable for the development of plans

about 27,000 MWh/y and a peak of about 30 MW were estimated, related to DC systems. The areas with the highest density were fur-

conﬁrming the signiﬁcance of space cooling needs in relation to the ther outlined. This information was integrated by other data related

climatic features and the extension of the considered territory. to the characteristics of the territory, including features of the nat-

The location of the ﬁnal 238 users is reported in Fig. 10 together ural and built environment. This is a delicate stage and implies a

with the representation of the density of the electricity needed for deep knowledge of the territory, of its characteristics and of the

space cooling in MWh/y per hectare. Analogously Fig. 11 reports context of operation. At the end, three main areas suitable for DC

their location together with the representation of the peak power were identiﬁed (namely Grancia, Lugano Paradiso, Piana Vedeggio),

as reported in Fig. 12. These three areas present densities between

10 and 20 MWh/y per hectare and precisely: Grancia 18 MWh/y per

hectare; Lugano Paradiso 14 MWh/y per hectare and Piana Vedeg-

Based on the expertise of the authors, this can be assumed as a mean value

gio 12 MWh/y per hectare, on average.

for the context of Canton Ticino. In Section 2.1 a lower value (i.e. equal to 2.5) is

Grancia has a high density but space cooling demand is almost

reported. This value was lightly increased (to 3) in order to consider the technologic

improvements of the last years in air conditioning systems. all concentered in an important commercial center. In this case,

L. Pampuri et al. / Sustainable Cities and Society 23 (2016) 23–36 33

Fig. 11. Representation of the density of electricity power for space cooling (red dots represent the 238 selected cooling users). (For interpretation of the references to color

in this ﬁgure legend, the reader is referred to the web version of this article.)

further investigations are needed in order to accurately verify the for DC. Furthermore, in this area there is an important energy

feasibility of a DC grid. demand related to refrigeration, mainly for the operation of phar-

The area of Lugano Paradiso is interesting because it is located maceutical industries. Despite the present research is focused on

near the net for the distribution of industrial water. Further inves- space cooling and does not include the analysis of refrigeration, this

tigations are needed in order to understand the actual potentiality point represents another important synergy toward the develop-

of DC in this area. As a disadvantage, this area includes many users ment of DC. In fact, it is estimated that refrigeration implies a further

and logistic difﬁculties related to morphology and mobility. electricity consumption of about 5,000 MWh/y with an average

Despite it has the lowest cooling density of the three, Piana density of 38 MWh/y per hectare. This potential can be added to

Vedeggio represents the most suitable area for DC. In this area a that related to space cooling as previously described and reported

project of DH has been approved; this could bring important syn- in Figs. 10–12.

ergies toward the realization of DC. Further, the area is near to In all three areas, however, it deserves to be pointed out that the

Vedeggio River and Lugano Lake that are both interesting sources analysis here reported did not take into account users that are not

Table 3

Users selected as suitable for DC: typology, number, yearly electricity consumption and peak power for space cooling.

Category Number Electricity for cooling [MWh/y] Max power for cooling [kW]

Engineering ofﬁces 1 32.4 23.5

Camping 1 29.5 69.5

Public buildings 1 9 23.6

34 L. Pampuri et al. / Sustainable Cities and Society 23 (2016) 23–36

Table 3 (Continued)

Category Number Electricity for cooling [MWh/y] Max power for cooling [kW]

Surgeries 1 8.6 7

Airports 1 5.5 7.4

Customs 1 2.6 8.4

Schools 2 43.6 42.6

Bakeries 2 29.4 58.1

Utility companies 2 21.1 23.9

Switchboards 3 276.2 160.4

Sport centers 4 84.7 135.6

Hotels closed in winter 7 208.1 275.7

Hospitals 8 1,353.6 1,200.6

Restaurants 11 155.8 177.7

Hotels 12 1,026.1 665.7

Pharmaceutical industries 12 280.5 552.1

Banks 16 667.5 734.1

Automotive mechanic workshops 16 131.2 175.8

Shops 17 763.8 1,174.9

Industries 18 771.8 967.9

Alimentary shops 32 890.8 978.1

Commercial ofﬁces 34 1,139.2 1,527.4

Common facilities in residential buildings 36 1,139.1 1,459.0

Total 238 9,070.1 10,448.8

Fig. 12. Representation of the three suitable areas for DC (red dots represent the 238 selected cooling users). (For interpretation of the references to color in this ﬁgure legend,

the reader is referred to the web version of this article.)

L. Pampuri et al. / Sustainable Cities and Society 23 (2016) 23–36 35

served by AIL SA and users that have a yearly electricity consump- The applied methodology could be easily replicated in other con-

tion lower than 100 MWh, as reported in Section 3.1. The inclusion texts of Switzerland, Europe etc. In Europe, the recent directives and

of other users could drastically increase the cooling density and declarations (European Parliament, 2009; European Parliament,

could improve the cost effectiveness of DC. This is particularly 2010; European Parliament, 2012; European Commission, 2014)

important because even if space cooling in Swiss residential build- request drastic improvements of the energy systems towards more

ings is not a standard application up to now and in many cases not efﬁcient paradigms and the same can be stated for Switzerland, as

necessary, it is still more and more used especially in highly insu- reported by BFE (2015), and in other countries with analogous eco-

lated dwellings, where comfort cooling becomes more important nomic and technical features. In this framework, national or local

due to higher thermal loads and rising summer thermal comfort authorities are advised to develop integrated heating and cooling

demands (Dott, Wemhöner, & Afjei, 2011). strategies. Combined district heating and cooling makes more efﬁ-

cient and cost effective the use of energy resources (El Barky &

Dyrelund, 2013; Ascione, Canelli, De Masi, Sasso, & Vanoli, 2014;

5. Conclusions

Jing et al., 2014).

Despite the energy efﬁciency measures developed in the last Local authorities should plan district heating and cooling

years, the energy consumption in buildings continues to rise. Space devices as an integrated part of the urban infrastructure whenever

cooling represents an increasingly important energy demand in the it is cost effective. DC should be considered as well as DH in energy

built environment, even in moderate climates. Space cooling is usu- planning, toward the coordination of energy supply and structural

ally provided by electric driven appliances and the related energy development of the territory.

consumption is often hidden and understated. District strategies could contribute to create the opportunity to

The novelty of this research lies in the adopted approach that meet low energy and low carbon standards in a more cost effec-

starts from a large amount of electricity consumption data, suitable tive way using renewable energy and efﬁcient combined heat and

for statistical processing, rather than from methods for predicting power via the district heating and cooling grids. DC, combined

cooling loads in buildings (energy simulations, energy audit etc.). with DH, distribute generation and cogeneration systems with a

Many authoritative researches are available in this ﬁeld but a signif- high integration of renewable energies, can represent an impor-

icant level of complexity is recognized. For example, Damnu et al. tant alternative or a complementary element for guarantying the

(2013) stated that it is not feasible to apply the traditional predic- energy goals at year 2030 and 2050 with a community approach

tion method to evaluate hourly building cooling load at the urban instead of a building approach. The building approach is indeed

energy planning stage because of the limited building information focused on nZEB standards that are difﬁcult to be rapidly applied

and complexity of energy prediction. After describing and testing to most of the existing built environment since important barriers

a simpliﬁed prediction model, Damnu et al. (2013) analyze the are still present (Caputo & Pasetti, 2015; Sesana et al., 2015).

potential causes of prediction errors and the signiﬁcance of various

cooling load inﬂuence factors, as demonstration of the complexity

Acknowledgements

of the issue.

Further researches about methods and tools for investigating

Many thanks to AIL SA for supporting the research, for provid-

energy consumption due to space cooling demand at urban or

ing all the available data and information and for endorsing the

regional level are needed. The research presented in this paper

methodological approach.

would like to give contributions to this need. The research reports

an interesting case of study in the territory of southern Canton

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