AN EXERGY-BASED SIMULATION TOOL FOR RETROFIT ANALYSIS IN

SCHOOL BUILDINGS

Ivan Garcia Kerdan1, Rokia Raslan

2, and Paul Ruyssevelt

1

1Energy Institute, UCL, London, UK

2Bartlett School of Graduate Studies, UCL, London, UK

i.kerdan.12@ucl.ac.uk

Keywords: exergy analysis, retrofits, non-domestic buildings, heating systems

ABSTRACT

Exergy represents the potential of maximum work obtainable from an energy transformation process

(the quality of energy). This concept allows us to

understand quantitatively and qualitatively any

energy process by identifying thermodynamic losses

that could be avoidable. In current buildings there is

a great potential to optimize systems and minimize

these losses by performing exergy analysis. The

following presents a proposed overarching structure

for an exergy analysis tool based on an exergy-based modelling approach for retrofit analysis in buildings.

This tool is integrated with a widely-used building

performance simulation tool (EnergyPlus) and the

ECB Annex 49 exergy method. This framework

allows the comparison between different retrofit

options and assesses the exergy efficiency of

different heating systems as well as locating where

the exergy is consumed along the energy supply

chain. To test the proposed tool, an archetype UK

school building was developed and used to assess a

base case scenario and five different retrofit options. Preliminary results illustrate the benefit of developing a dynamic exergy simulation tool with

the intention to facilitate the assessment of the true

thermodynamic inefficiencies and determine the best

quality match between existing local energy sources

and the required quality within the UK non-domestic

sector.

INTRODUCTION

Approximately half of the energy used in the UK is

dedicated to space and water heating purposes,

where buildings are responsible for 52% of the

overall energy used (DECC, 2013). Since indoor

temperatures usually range between 18-25 C, research indicates that the heating needs of buildings

can be met by low-grade heat sources. However, a

key issue associated with this application is that of

the ineffective match between the potential of the

sources and the quality demand of the buildings

(Schmidt, 2004). The concept of energy efficiency

through the first law does not provide a real

indicator of how nearly a system approaches to

100% thermodynamic efficiency or true ideality

(absence of thermodynamic losses or

irreversibilities) (Szargut, 1980). An irreversible

process is a process that cannot return the system

and the environment to their original conditions

because of the constant increase of entropy in the environment. This phenomenon is common in any

real thermodynamic process, like those found in the

built environment. To support this, the second law of

thermodynamics states that in every process where

energy or matter is dispersed, entropy is inevitable

generated; this means that exergy can actually be

lost due to the irreversibilities of a process.

Consequently, it can be suggested that exergy

analysis (a combination of first and second law of

thermodynamics) can become essential in locating

the aforementioned inefficiencies and seeking opportunities for improvement. To determine these

inefficiencies, the use of a holistic approach is

necessary to establish the amount of exergy that is

consumed throughout all the main parts of building

energy systems (Shukuya, 1994). Buildings, similar

to any energy system, work as an exergy-entropy

process. A building and their systems basically feeds

on exergy, consumes exergy, generates entropy, and

the generated entropy is finally disposed of into the

environment. Disposing of the generated entropy

from the system creates new an opportunity for

feeding on exergy and consuming it again, thus the process cycles.

The reference environment

The most important concept that has to be taken into

consideration to undertake an exergy analysis of a

given system is the establishment of a reference environment (Hermann, 2006). This because exergy

is not only a property of the system but also of the

environment. Hence, an exergy calculation primarily

depends on the choice of reference environment and

this is determined through the implementation of

preliminary analysis to identify which environment

can act as an entropy-disposal sink. Therefore,

through the use of exergy with the support of the

second law and the Carnot formula1, the quality or

1The Carnot formula sets the limiting value on the fraction of the heat which can be used.

usefulness part of energy to produce power from

heat can be obtained (Eq. 1).

(1)

In the Carnot formula, represents the reference environment temperature (in absolute value [K]). An

essential characteristic of the reference environment

is that has to be irreversibilities-free, where all the major exergy destructions should occur in the

system or process analysed. Toro et al. (2009)

explained that the biggest debate surrounding

entropy-disposal sinks is that between the ground and the surrounding outside air. Both can be considered as infinite sinks, but the latest it is always

available and does not suffer any changes in its

physical properties (thermal, chemical) due to

interaction with buildings. In this paper, outside air is also considered as the reference environment.

Exergy utilization in the UK building sector

At the present time, the UK is still largely dependent

on fossil fuels and electricity to meet the energy

demand in buildings (especially for space heating).

Since the most common heating system technologies

used in the building sector (e.g. furnace, gas boilers,

condensing boilers, electrical heating, and air

conditioners) require high-grade sources (e.g. natural

gas, electricity), the low quality demand is mismatched by the utilization of these high-grade

sources. In the UK, several researchers (Hammond

and Stapleton, 2001, Gasparatos et al., 2009) have

used statistical exergy approaches to analyse the

exergy utilization across the UK sectors. The

analyses show that the building sector has low

exergy efficiency compared to other economic

sectors in the UK (Figure 1). This is understandable

because in most industrial processes, optimization

through the implementation of exergy calculation is

commonly applied (Rosen, 2002).

Figure 1 Exergy Efficiency in different UK sectors

(Modified from Gasparatos et al., 2009)

The importance of the exergy method for

building retrofits

To improve exergy utilization within the building

sector, actions have to be implemented with regard

to both existing buildings and their energy

infrastructure. Improving exergy efficiency

throughout the building sector can deliver a large

range of benefits in the economy, environment and

society. This will only be achievable if buildings are

transformed through a comprehensive, rigorous and sustainable approach. The issue of building retrofit

optimisation should focus on the determination and

application of the most cost effective technologies

without compromising the delivery of service and

comfort at an acceptable level (Ma et al., 2012). The

constant tightening of building energy regulations

has led to the design of new and more efficient

energy systems; although the majority of

regulations, codes and even systems are developed

based only on the first law analysis. As the

retrofitting of buildings through an exergy approach

(Figure 2) has a significant impact on the national energy security, more national policies and

incentives that consider the maximization of exergy

utilization and the reduction of exergy destructions

at all levels of the energy supply chain should be

therefore implemented .

Figure 2 Schematic exergetic flow comparison

between a conventional building and an exergy-

efficient building (Modified from Annex 49, 2009)

Exergy simulation tools for buildings

Building modellers regularly use simulation and

optimization software to help perform calculations that result in design parameters for buildings and

their systems, however the consideration of the

whole energy supply chain in the analyses is not

commonplace. Some steady-state tools have been

developed with the intention of calculating exergy

consumption in building systems (Sakulpipatsin and

Schmidt, 2005, Schlueter and Thesseling,2009). As

is noted in the final report of the ECB Annex 49, a

steady-state assessment can only be used to get a

first comparison between systems but contains high

uncertainty on the results. Although dynamic

simulations involve longer times to run the models and are far more complex than static approaches,

they are required for an accurate comparison

between projects and serve for optimization of

building systems, especially those related to energy

storage. Some studies (Angelotti et al., 2009, Toro

et al., 2009, Sakulpipatsin et al., 2010) have

conducted dynamic simulations at a preliminary

stage showing an initial assessment of the potential

strengths and limitations that dynamic analysis have

for the exergetic optimization in buildings. Recently, the DPV project (Schlueter, 2013), a dynamic

simulation add-in for a BIM tool (Autodesk Revit)

that performs energy and exergy analysis for an

early design stage was developed.

MODELLING FRAMEWORK

Figure 3 illustrates the proposed modelling

framework, which is intended to be an extension of

the aforementioned currently available models.

Based on a review of developments in this field, it

can be assumed that in the near future, the

regulatory compliance process for building retrofit may potentially require that an exergy assessment

be carried out. This already is the case in the Canton

of Geneva, Switzerland, where the local legal

framework stipulates that the documents required

from city developers (including building retrofit

projects) should include an exergy approach (Favrat

et al., 2008). If this happens in the UK in the near

future, the modelling framework presented here

could serve to support similar legal requirements.

This modelling framework for a proposed

simulation tool was developed with the aim of helping to analyse the exergy efficiency and exergy

consumption before and after energy-oriented retrofits are implemented in non-domestic buildings.

This simplified methodology will allow the

implementation of robust exergy analysis, assess the

impact of refurbishment actions on exergy

consumption, and attempt to define exergy

benchmarks based on building archetypes. The

proposed tool performs exergetic analysis for

building retrofit based on a bottom-up building

physics approach.

The model environment is based on, the energy simulation tool EnergyPlus (US DOE, 2012)

coupled with a python-based program add-on

developed to combine dynamic energy simulations

with the Annex 49 exergy analysis method. In the

modelling approach, EnergyPlus will calculate the

heating and cooling loads necessary to maintain the

thermal control setpoint. The energy tool, enables

the calculation of the energy demand (hourly step)

dQ, the inside temperature and outdoor temperature

T (from the TMY2 weather files) parameters.

Exergy flows related to heating and cooling demand in buildings are very sensitive to the choice of the

reference state, since HVAC systems operate very

close to the dead state (Angelotti et al., 2009).

Following the calculation of these parameters, an

exergy analysis is performed throughout the supply

chain. In the first instance, the exergy load in the

room is calculated as follows:

(2)

According to the system analysis method developed

by Schmidt (2004), the subsystems of a building

heating chain can be differentiated into six

subsystems, where the primary energy transformation subsystem is located outside the

buildings boundary. The calculation must be performed in the opposite direction, starting from

the envelope and ending in the conversion of

primary energy. The demand of each subsystem

must be satisfied by the subsystem that precedes it.

When the supply passes through the energy chain,

losses are expected throughout all subsystems, this

is dependent on such factors as the envelope

characteristics or heating systems components. In

this work, this method is automated and combined with the common energy analysis. Further

information regarding the method and calculations

can be found in relevant documentation (Annex 49,

2011).

Retrofit scenarios module

A module that encompasses a variety of common

retrofit measures applied to non-domestic buildings

at both the building and energy supply infrastructure

level was developed. Based on the Annex 49

research, the most important subsystems in the

energy supply chain of a building are identified; the

module is then used to develop different

refurbishment measures at each level of the supply system. These are then simulated and analysed to

provide an understanding of the impact of both

individual and a group of measures on the exergy

consumption on the whole system and subsystems.

The retrofit scenarios module is divided into the

following group of technologies:

1. Building envelope retrofits 2. Heat emission (convectors) retrofits 3. Distribution system retrofits 4. Storage system retrofits 5. Generation system retrofits 6. Primary energy transformation options

The first five groups are building level (although

groups 3, 4 and 5 can also be found at community

level), while group 6 looks at all the actual national

energy resources available that have the potential to

cover and/or assist in meeting the demand at the

highest exergy-efficiency possible. One of the main

objectives of the model is to focus on large energy transformation systems (e.g. CHP) and compare it

with small and micro systems (micro-CHP or heat

pumps). This helps analyse the impact of different

heat sources and the impact of retrofitting at the

building level and supply chain.

Figure 3 Proposed methodological framework for exergy analysis of retrofit projects

CASE STUDY

For this study, the UK educational (school) sector

was chosen to perform an exergy analysis.

Consequently, a group of archetypes that represent

the most common characteristics in the sector was created. The method for the development of

archetypes was based on a process that involved a

review of relevant literature and the application of

statistical analysis to find the most important

variables associated with energy use (Famuyibo et

al., 2012). For the development of archetypes

relevant to the UK context, key UK data sources

were considered (Steadman et al., 2000a, Steadman

et al., 2000b). Based on this, 5 school buildings

sizes/form configurations were identified:

1. 1,500 m cellular sidelit 2. 7,500 m cellular sidelit 3. 7,500 m cellular around open plan 4. 15,000 m cellular around deep plan 5. 15,000 m cellular sidelit

Table 1 presents the most important characteristics

of the UK educational (school) sector.

Table 1 Characteristics of the UK educational sector

Characteristics Value Unit

Primary school mean area 1518 m

Secondary School mean area 7452 m

Most common form Daylit

cellular

-

Most common structure Frame -

Walls/Floor Ratio 0.6 -

Glazing/Wall Ratio 0.28 -

Number of floors (mean) 2.44 floors

For this study, a simplified version of a single-zone

1,500 m2 primary school building was developed

(Figure 4). This model included both external and

internal thermal mass characteristics, and typical

patterns on occupation, operation schedules, and miscellaneous equipment

Figure 4 Simplified archetype model of a UK

primary school

SIMULATION

To test the modelling framework, different retrofit

options for the different subsystems of the energy

supply chain in a typical school were assessed.

Although the impact of the building geometry, form, and characteristics was considered, the main focus of

the analysis is the energy supply systems. The

thermal building characteristics of the model were

based on past and current UK building regulations.

For the heating systems, a configuration comprised

of a standard gas boiler (the most common heating

system in the sector), a water to water local heat

pump, and a hypothetical connection to a district

heating network was modelled. Table 2 lists all the 5

retrofit cases used in the study.

Table 2 Main characteristics analysed for the baseline scenario and five retrofit options

Building and system

Characteristics Baseline Case A Case B Case C Case D Case E

External wall (W/mK) 1.7 0.17 1.7 1.7 0.17 0.17

Roof (W/mK) 1.42 0.19 1.42 1.42 0.19 0.19

Ground floor (W/mK) 1.42 0.25 1.42 1.42 0.25 0.25

Glazing (W/mK) 5.7 1.3 5.7 5.7 1.3 1.3

Infiltration (ach) 1.05 0.35 1.05 1.05 0.35 0.35

Glazing % 30% 30% 30% 30% 30% 30%

Lighting load (W/m) 12 12 12 12 12 12

Miscellaneous Load (W/m) 5 5 5 5 5 5

Heating System Gas Boiler Gas Boiler Ground heat

pump W/W District heat

Ground heat

pump W/W District heat

Source Natural Gas Natural Gas Electricity and

low grade heat Waste heat

Electricity and

low grade heat Waste heat

Thermal efficiency 0.8 0.8 4.61 0.89 4.61 0.89

Primary energy factor fossil FP,fossil 1.1 1.1 2.7 0 1.7 0

Max. supply temperature qS1,max [C] 90 90 60 70 60 70

Emission System Radiator

HT 90/70

Radiator

HT 90/70 Wall heating

Slab and

floor heating Wall heating

Slab and

floor heating

Inlet temperature qin [C] 70 70 50 28 50 28

Return temperature qret [C] 60 60 40 22 40 22

Heat loss / efficiency hE [-] 0.95 0.95 0.95 0.99 0.95 0.99

For the baseline scenario a poorly insulated building

coupled with a gas boiler heating system was

considered. Case A represents the same building

model but with high thermal insulation levels as

required by the current England and Wales

regulations (Part L2). Case B and Case C have the

same thermal properties as the baseline scenario but with upgrades to the generation and heating

emission (radiators/convectors) subsystems. For

Case B, a water to water ground heat pump was

analysed and the typical 90/70 radiators are replaced

by a wall heating convection system with an inlet

and outlet temperature of 50 C and 40C

respectively. Case C analyses the possibility of

connecting the school heating system to a

hypothetical district heat network, where waste heat

from electricity generation could be used a heating

source. This useful low-exergy energy sources could

represent a vast potential for a future low-carbon heating system. To maximize the potential of district

heating, the heat emission system was replaced by

slab and floor heating with an inlet and outlet

temperature of 28 C and 22 C respectively. Case D

and Case E are similar to B and C but the building

envelope was retrofitted to the latest regulatory

requirements (as in Case A).

For this experiment, the climate of London was

considered and the simulations were performed

using the relevant weather file (TMY2). The

temperature data from this file was used as the reference temperature for the dynamic exergy

analysis. The framework described in the

methodological section was tested (Fig 3), but with

the difference that a partial dynamic simulation was

performed (results from only two months, January

and March, are presented, January and March).

Although this can be considered to be a limitation

affecting the interpretation of the outputs, the results

produced are considered sufficiently valid to enable

the production of insights on how the proposed

framework works. The result analysis module was

carried with the help of a spreadsheet and the R

software.

RESULTS AND DISCUSSION

The first two graphs in this section show the exergy

demand calculated throughout the whole year. This

exergy demand is estimated by multiplying the load

demand by the quality factor estimated by the Carnot

formula, using outside and inside temperature (eq.

2). Figure 5 represents the exergy demand of the

baseline scenario, (poorly insulated school), while

Figure 6 represents the same building but insulated to the latest Part L standards. It can be seen a large

decrease the annual exergy demand of the building.

Also, in both cases, the exergy demand is notably

less in the summer months when the inside

temperatures are closer to the reference

environment.

Figure 5 Exergy demand, Inside and Outside

Temperature for a poor insulated primary school

Figure 6 Exergy demand, Inside and Outside

Temperature for a high insulated primary school

As stated in the introduction section, information

regarding the irreversibilities and thermodynamic

losses cannot be provided by energy analysis only.

This has to be complemented by the analysis of

exergy consumption and destruction at each

subsystem of the supply chain. The following graphs illustrate the location, the magnitude, and the

sources of thermodynamic inefficiencies in the

systems. The results are presented for all 6 cases for

January and March. These graphs provide an

understanding of the difference in the carbon

footprint of a highly insulated building (Part L) with

conventional heating systems (gas boiler, electric,

HVAC) and a poorly insulated building with other

heating systems (gas boiler, CHP, heat pump, district

heating).

January

Figure 7 and 8 show the total exergy destruction

added by subsystems and the exergy flow

throughout the supply chain, respectively. It was

expected that the results would show a reduction in

irreversibilities due to energy oriented retrofits at all stages; however the results show the real impact of different types of retrofit measures on the entire

supply chain. In comparing Case A (only high

insulation) and Case C (district heating and heat

emission retrofit) it is obvious that these two

different types of refurbishment projects reduce

losses by around 66%, however some differences

between them can be seen if the whole supply chain

is analysed. Case A presents similar amount of

exergy destruction (~20 kWh/m) at the primary

transformation and generation subsystem (high

grade fuel used in a gas boiler). On the other hand, more than half of the exergy destruction of Case C

occurs at the transformation subsystem, where high-

grade fuel is used at the power plant where heat

waste is generated. For Case B (heat pump) the

losses at the transformation system are even greater

because of the necessity of electricity in this type of

technology (electricity has one of the highest energy

qualities, 1.0). Case D and Case E (holistic retrofits) show minimal differences in exergy

savings comparing to Case A and Case C, showing that they are impractical and probably not a cost-

effective solutions.

Figure 7 Total exergy destruction at each stage of

the supply chain (January).

Figure 8 Supply chain exergy flow analysis-

degradation of the energy quality (January)

March

The same analysis is shown for the month of March,

to show the differences in exergy consumption,

destruction and efficiency between two different but

not very distant periods of the year. The difference

in temperature is smaller than in January, and this

can be seen in the reduced energy quality demanded

by the building (Figure 5 and 6) and the reduction in exergy losses by all subsystems in all retrofit cases

(Figure 9). Similar exergy destruction trends were

expected (compared to January) but an interesting

difference is found in comparing Case A and C. In

this month, Case A has lower total exergy

destruction than Case C. Also it should be noted that

holistic retrofits bring minimal gains compared to

projects where only the envelope or the HVAC

system is retrofitted.

0

20

40

60

80

100

120

140

Baseline Case A Case B Case C Case D Case E

kW

h/m

Scenarios

Envelope

Inside Air

Emission

Distribution

Storage

Generation

Transformation

0

20

40

60

80

100

120

140

kW

h/m

Energy Supply Chain

Baseline

Case A

Case B

Case C

Case D

Case E

Figure 9 Total exergy destruction at each stage of

the supply chain (March).

Figure 10 Supply chain exergy flow analysis-

degradation of the energy quality (March)

To complete the analysis, exergy efficiencies must

be calculated. This illustrates how far the building

and their systems is from an ideal thermodynamic

stage. The total exergy system efficiency is obtained

in a similar manner to the energy efficiency; in this

case the exergy that leaves through the envelope is

divided by the total exergy load of the room. This

displays the ratio of exergy that it is really being

used (Eq. 3). At the building level, a useful indicator

is produced by comparing the exergy supplied by the

local HVAC system and the exergy leaving the building (Eq. 4).

(3)

(4)

The results for all the cases and the analysed months

are presented in table 3.

Table 3 Exergy efficiencies at supply level and

building level for January and March

January Base A B C D E

total (%) 2.1 1.1 3.9 6.2 1.4 1.7

building (%) 2.8 2.0 11.9 18.7 4.3 5.0

March Base A B C D E

total (%) 1.3 0.4 2.4 3.5 0.5 1.7

building (%) 1.8 0.9 7.1 10.4 1.4 5.0

As can be noted by the low exergy efficiencies,

buildings supply side are far from being ideally compatible with the demanded exergy requirement

from buildings ( > 10%). From this analysis, Case A has the lower exergy efficiency of all cases

analysed. The main reason is that although exergy

destructions are minimized by the building envelope,

the whole supply chain is still dependant on high-

grade fossil fuels where large differences of

temperatures can be found in the transformation

processes. On the other hand Case C represents the

most ideal case in exergy terms. Here exergy

destruction is minimized due to the better match

between the quality of the supply source and the

exergy demanded by the building to maintain minimal comfort conditions (waste heat at ~70 C to

heat a room at ~20C).

CONCLUSIONS

This paper presents a first scope on the potential of

different retrofit scenarios to reduce exergy losses in

the UK non-domestic sector. Exergy can become a

powerful and useful tool to aid in the

implementation and undertaking of more robust

retrofit projects. Is evident that this approach can be

used in retrofitting the supply chains as it gives a

better perspective in where exergy is being wasted.

The results indicate that a building with no insulation but connected to a district heating system

wastes less exergy, that results in the generation of

less CO. These benefits occur by using heat waste in district heating as it has a low environmental

impact due to heat recycling and use of renewables.

Additionally, the heat can be produced through

many different production methods and is not

dependant on a specific fuel type. If this scenario

were to be replicated for the building sector as a

whole, conventional retrofits could become

economically non-viable and the major benefits could be seen at a national level in better energy

resource utilization. On the other hand, highly

insulated buildings can improve the ability of a

school building to utilize low quality energy sources,

although the capital cost of installing all measures

could make a project of this type not profitable.

Finally, heat pumps (Case B and D) also have a

large utilization potential if a district heating

network is not available near the building site.

0

20

40

60

80

100

120

140

Baseline Case A Case B Case C Case D Case E

kW

h/m

Scenarios

Envelope

Inside Air

Emission

Distribution

Storage

Generation

Transformation

0

20

40

60

80

100

120

140

kW

h/m

Energy Supply Chain

Baseline

Case A

Case B

Case C

Case D

Case E

Although the dynamic analysis approach is more

time consuming, it allows a more accurate

comparison of different options in the supply chain,

especially in calculations where the reference

environment is closer to the indoor temperature (e.g.

in March). If exergy analysis is going to become part of retrofit analysis in buildings, it is essential the

development of new methods to calculate the

economics of exergy destruction in buildings. In this

sense, thermoeconomics represent a valuable method

for the optimization of retrofit options. For future

work, a module will be developed within this

modelling framework. On the other hand, is

important to note that the exergy method by nature is

more focused on energy utilization/optimization and

commonly neglects other non-energy benefits such

as reduction of operational cost and improvement of

internal comfort. More research is needed to understand how the exergy approach impacts these

parameters.

NOMENCLATURE

Quality or Carnot factor (-)

Temperature (K)

Exergy or second law efficiency (-)

Exergy consumed by the room air (kW)

Total exergy supplied to the system (kW)

Exergy input at the generation system (kW)

ACKNOWLEDGMENT

The first author acknowledges support from The

Mexican National Council for Science and

Technology (CONACyT) through a scholarship to

pursue graduate studies.

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