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Energy & Buildings 320 (2024) 114601
Available online 26 July 2024
0378-7788/Crown Copyright © 2024 Published by Elsevier B.V. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-
nc/4.0/).
Renewable energy allocation in Multi-Owned Buildings for Sustainable
Transition: A novel Evidence-Based Decision-Making framework
Aravind Poshnath
a
,
b
, Behzad Rismanchi
a
,
*
, Abbas Rajabifard
b
a
Renewable Energy and Energy Efciency Group, Faculty of Engineering and IT, The University of Melbourne, VIC 3010, Australia
b
Centre for Spatial Data Infrastructures and Land Administration, Dept. of Infrastructure Engineering, The University of Melbourne, VIC 3010, Australia
ARTICLE INFO
Keywords:
Energy entitlement
Renewable energy allocation
Evidence-based Decision-Making
Suitability analysis
TOPSIS
Multi-owned buildings
ABSTRACT
Adopting Renewable Energy Systems (RES) in Multi-Owned Buildings (MOBs) is critical for achieving sustain-
ability goals, yet the equitable allocation of energy from a jointly-owned RES to individual apartments remains
overlooked in practice and literature. Current practices, rooted in models of common cost allocation, fail to
address the dynamic traits of energy allocation and disregard the energy entitlement of each apartment,
necessitating a tailored approach for renewable energy allocation in MOBs. This paper emphasises energy
entitlement and introduces a novel, evidence-based decision-making framework assessing nine distinct energy
allocation models for their suitability in diverse MOB typologies, characterised by physical and social factors, and
presents a ranked list. Our ndings reveal extensive variation in model suitability depending on the building
typology. Equal energy allocation minimised nancial disparities, while demand-based allocation was signi-
cantly effective for older, mid-rise buildings with fewer tenants. Conversely, the at-fee model was found un-
suitable regardless of building type. Furthermore, the study demonstrates that the suitable model for a building
typology may not always align with the objectives of RES installation, thus endorsing an ‘objective proximity’
analysis. The proposed framework serves as a valuable guide for stakeholders, including the owners’corpora-
tions, policymakers, and industries, to make well-informed decisions for a smooth transition to renewable en-
ergy. It lays the foundation for potential expansion to other building types while underscoring the necessity for
adaptive policies in promoting RES adoption.
1. Introduction
The sustainability movement is gaining momentum globally,
augmented by several pledges to attain net-zero status. The formal
adoption of Sustainable Development Goals (SDG), notably SDG 7
(Affordable and Clean Energy), SDG 11 (Sustainable Cities and Com-
munities), and SDG 13 (Climate Action) emphasises the crucial role of
cities in achieving sustainability. However, the exponential surge in city
dwellers demands the vertical expansion of cities, exemplied by
remarkably high vertical living approvals [1]. The increase in energy
demand due to the burgeoning population mandates enhanced energy
efciency of apartments and decentralisation of renewable energy
generation to meet sustainability goals. Despite these imperatives, the
installation of Renewable Energy Systems (RES) in multi-owned build-
ings (MOBs) faces practical barriers and limited policy support, resulting
in sub-optimal utilisation of solar potential [2].
The adoption of RES, often located in the common properties (CPs) of
the MOBs, demands collective consensus among members of the
‘Owners’Corporation’(OC). The mandatory physical, legal, social and
economic consensus of other residents [3] discourages individual ef-
forts, while the distribution of costs and benets remains ambiguous,
complicating energy management [4]. The ‘Energy Entitlement’concept
has been proposed to address these challenges by delineating the
ownership of renewable energy from a commonly-owned RES to indi-
vidual apartments, empowering the residents with control over their
allocated energy [5].
Additionally, the RES installation is impeded by the dearth of liter-
ature and legislative backup on energy allocation. The current energy
allocation models, primarily based on the prevailing practices of com-
mon cost allocation within MOBs, such as equal or lot entitlement-based
allocations, may prove inefcient in attaining net-zero objectives.
Furthermore, the suitability of these energy allocation models is
contingent upon the building typology, ranging from the external at-
mospheric conditions to the dweller composition in the MOB.
* Corresponding author.
E-mail address: brismanchi@unimelb.edu.au (B. Rismanchi).
Contents lists available at ScienceDirect
Energy &Buildings
journal homepage: www.elsevier.com/locate/enb
https://doi.org/10.1016/j.enbuild.2024.114601
Received 7 May 2024; Received in revised form 18 July 2024; Accepted 23 July 2024
Energy & Buildings 320 (2024) 114601
2
The research on the energy allocation models and their suitability for
MOB typologies is in the nascent stage. Certain studies recommend
equal allocation [6] or investment-based allocation [7], founded for
simplicity. However, these models fail to account for the diversity of
building characteristics, such as the building age and height, tenant
proportions, or common property ownership. Moreover, prevailing
allocation models overlook the unique characteristics of energy demand.
This study seeks to bridge these gaps by not only evaluating the tradi-
tional equal and investment-based allocation models, but also exploring
several novel approaches to energy allocation. We systematically anal-
yse the performance of these models across a range of MOB character-
istics such as age, ownership structure of CP, tenant proportion, and
height of MOBs, ensuring wider applicability of research outcomes.
Despite extensive research on the utilisation of untapped solar en-
ergy, there prevails a substantial gap concerning the ownership of en-
ergy within multi-owned properties –a persistent challenge inhibiting
the installation of RES. As policies increasingly target MOBs, encour-
aging the adoption of RES, they simultaneously create a breeding
ground for ownership-related disputes. This study centrally addresses
the intricacies of energy management within MOBs, deliberately
excluding the interaction with grid power due to the extensive research
exploring the topic. This study seeks to initiate a dialogue on the critical
issue of energy entitlement by proposing potential energy allocation
models.
The primary objective of the study is to develop an evidence-based
decision-making framework to assess the suitability of the energy allo-
cation models for distinct building typologies. The study introduces and
evaluates several novel and existing allocation models, enhancing the
breadth of the analysis. Furthermore, the research incorporates
advanced numerical modelling techniques to estimate the ‘cost of
inaction’and ‘nancial disparity’associated with each model and em-
ploys a scientically driven Multi-Criteria Decision Analysis (MCDA)
tool to identify the most feasible energy allocation model for the
building, contributing to the novelty of the work. The study also con-
templates the inuence of the installation objective of RES relative to the
suitability of the building typology. This evaluation serves as a guiding
tool for several stakeholders, such as the OCs, policymakers, and in-
dustries, with the exibility to re-assess the ranking of the allocation
models based on overarching objectives of RES installation, such as
attaining net-zero goals.
The paper is structured into four sections: Section 2 highlights the
necessity to focus on the energy allocation regime while shedding light
on the existing energy allocation models and the necessity for an
evidence-based approach; the potential energy allocation models, the
inuence of building typology factors and the proposed evidence-based
decision-making approach are outlined in Section 3;Section 4 show-
cases the applicability of the novel framework using a hypothetical case
study, complementing the signicance of the framework; and Section 5
critically discusses the implications of the research, including the limi-
tations, advocating the necessity for further focus on the energy allo-
cation domain.
2. Energy allocation in Multi-Owned buildings
The ourishing MOBs in Australia, with 16 % of the population
residing in the strata properties [8], exhibit a signicant opportunity for
on-site renewable energy adoption. The ‘strata title’, also known as
‘condominium’, ‘unit title’, or ‘freehold’, confers the residents the
ownership rights of their apartments, a distinct share of the common
property such as rooftops, and membership in the OC. The RES are often
installed on the rooftops due to limited space and necessitate the col-
lective consent from other owners. The issue is exacerbated when mul-
tiple OCs manage different CPs, often resulting in the abandonment of
installation proposals of RES and hampering the necessary energy
transition to support net-zero goals.
Numerous business models prevail for the installation and operation
of RES in MOBs, potentially avoiding the ownership conundrum, such as
leasing rooftops to third parties, purchasing a share of remote solar
power plants, and implementing embedded networks. However, these
models often require residents to enter multi-party contracts for longer,
restricting their freedom to switch operators and nullifying energy
ownership. While purchasing a share of remote solar panels benets an
individual homeowner, the rooftop solar potential of the MOBs remains
unutilised. Hence, this study concentrates on the prevalent procedure in
the strata ecosystem wherein the OCs manage and operate the RES, with
energy distributed to the apartment units based on their calculated
entitlement.
Fewer external walls tend to make MOBs more energy-efcient than
detached dwellings [9]. However, MOBs experience diverse peak loads,
leading to signicant uctuations in energy demand [10]. The instal-
lation of PV systems can mitigate this pressure on electric grids. Roberts,
et al. [11], demonstrated that integrating PV systems with centralised
batteries in MOBs can reduce grid dependency and peak demand by 12
% and 30 %, respectively. Similarly, Sørensen, et al. [12] revealed a
reduction in peak load by 45 % and met 38 % of electric vehicle charging
needs using PV-generated electricity in MOBs. Furthermore, a study
[13] on a ve-storey MOB reported a substantial 49.8 % reduction in
annual energy demand post-PV installation. These energy savings
translate into signicant monetary benets for the residents by reducing
reliance on grid electricity, as evidenced by Roberts, et al. [14]. The
targeted government incentives [15] can further alleviate the nancial
burden on the apartment owners and shorten the payback period.
Despite these benets, the adoption of RES in MOBs remains slow,
mainly due to disagreements between the owners [9].
The energy allocation models possess innate complexities that may
circumstantially impact the operational dynamics of MOBs. For
instance, the equal allocation of costs arising from a commonly shared
switchboard raised disputes in [16] due to consumption disparity among
stakeholders. The inconsistencies in lot entitlement based on the unit’s
storey were disputed in [17], where the mall frontage caused higher
contributions for lower oor units. The RES installations based on equity
led to disputes in [18], where the roof capacity for individual in-
stallations was questioned. Moreover, the units possessing substantial
lot entitlement may be able to inuence the decisions in their favour, as
seen in [19]. Similarly, the allocation based on dynamic factors such as
occupancy and energy prole may increase the uncertainties and de-
mand for frequent recalculation of energy entitlement. Dependency on
static entities such as oor area could directly indicate the units’energy
demand. However, assigning double the cost for double the area is
debatable [20].
The innumerable disputes related to common cost allocation spot-
light the inherent complexities of building-dependent energy allocation
models and highlight the necessity of opting for suitable allocation
models. Numerous studies incorporate the suitability assessment pro-
cedure across dimensions ranging from land-use planning to energy ef-
ciency in buildings. Suitability assessment aids in holistic decision-
making while exercising due diligence. In their study, Koulamas, et al.
[21]used the ‘High’, ‘Medium’, and ‘Low’classications to compare the
suitability of different energy consumption models. Similarly, Adedeji,
et al. [22] classied global solar resources for solar PV suitability
ranging from ‘Highly favourable’to ‘Least favourable’. Though the
suitability varies with circumstances, it is critical not to underrate the
potential inherent risks associated with the allocation models.
Numerous denitions of risk have surfaced across the literature, one
widely accepted being Equation (1). Due to the limited literature on the
energy allocation models owing to their novelty, this work focuses on
impact analysis to evaluate the inuence of impacts on the suitability of
allocation models while omitting the estimation of the likelihood of
occurrence of impacts [23,24].
Risk =Impact ×Likelihood (1)
Owing to the inuence of a multitude of criteria, a scientically-
A. Poshnath et al.
Energy & Buildings 320 (2024) 114601
3
driven approach is vital to arrive at a decision with the most minor
disputes. Analysing the scientic articles between 2010 and 2020, the
study conducted by Hajduk and Jelonek [25] revealed that TOPSIS was
among the most frequently used MCDA techniques. Biyik, et al. [26]
devised guidelines for choosing the suitable MCDA method and owing to
the single synthesis criterion and deterministic score, TOPSIS is one of
the appropriate models to utilise. Due to the validated effectiveness in
handling complex decision making scenarios [27], TOPSIS has been
Fig. 1. Evidence-based Decision-Making Approach for Selecting Suitable Energy Allocation Models in MOBs.
A. Poshnath et al.
Energy & Buildings 320 (2024) 114601
4
employed in the study to outline the most suitable energy allocation
model.
The multi-owned buildings have been devoid of renewable energy
support both in practice and literature. Roberts, et al. [28] paved the
way by focusing on the technical arrangements of solar PV on apartment
buildings, while Syed, et al. [29] concentrated on the performance of
solar PV systems. Certain studies explored realms ranging from eco-
nomic utility [30] to policy evaluations [31]. Although Akter, et al. [7]
and Lovati, et al. [32] briey addressed the energy allocation strategies,
a detailed focus was lacking. The dearth of such comprehensive analysis
on the energy allocation models is expected to be diminished through
this report, which presents a unique framework for selecting the most
suitable energy allocation model for a distinct multi-owned building
typology.
3. Methodology
The suboptimal adoption of RES in MOBs, coupled with scant liter-
ature and legislative attention on the energy allocation models, neces-
sitates a comprehensive methodology that factors various attributes of
energy allocation. This section introduces a novel evidence-based
approach encompassing the building typology-dependent suitability
analysis and impact analysis of the allocation models, with a ranked
order of the most feasible energy allocation model being the outcome.
The detailed methodology of the evidence-based decision-making
framework is illustrated in Fig. 1. Due to the similarity in nature of the
MOB management followed worldwide, the presented methodology is
poised to be applied irrespective of the region, with necessary modi-
cations owing to the pertaining legislations and subjective nature of the
owners’corporations.
3.1. Framework for the evidence-based decision making approach
The proposed framework amalgamates three distinct analyses,
relying on four critical input criteria: the age of the MOB, ownership
type of CP, tenant proportion and the height of the MOB in storeys. The
signicance of each building typology factor is detailed in Section 3.2.
Section 3.3 outlines the potential energy allocation models considered in
the study and their characteristics. A suitability database has been
crafted to analyse the characteristics of each energy allocation model
and their correlation with the building typology. The suitability scores of
each energy allocation model are consequently calculated by adopting
the process explained in Section 3.4. Additionally, an impact analysis is
performed to incorporate the inherent negative impacts of each energy
allocation model independent of the building characteristics. The results
of both the suitability and impact analyses are integrated to derive the
composite scores that give insights into the performance of energy
allocation models for the specic building typology.
Considering the diverse preferences of OCs, the framework is
designed with the exibility to modify the weightage of typology factors.
To ensure the consistency of the scores, the TOPSIS analysis is performed
−a proven methodology in a multi-criteria decision analysis regime,
aiding in ranking the suitability performance of energy allocation
models. The nal phase of the framework focuses on the attainment of
overarching goals such as net-zero energy status, for which the meth-
odology recommends a proximity analysis of the highly-ranked models
to evaluate their performances. This analysis reinforces an evidence-
based ranking system, empowering the stakeholders to make informed
decisions regarding the most appropriate energy allocation model for
the MOB.
3.2. Building typology denitions
The energy consumption of each building is correlated with the dy-
namic interplay between humans and buildings [33], alongside the
physical characteristics. The suitability of allocation models is
immensely intertwined with the building typology, which encompasses
social and physical attributes. This section outlines the factors incor-
porated in the study to dene the typology of a residential MOB.
3.2.1. Building age
The energy efciency standards have been regularly altered globally
to accelerate the attainment of net-zero goals. However, a signicant
stock of old residential buildings still adhere to outdated energy rating
standards, necessitating substantial retrots [34]. Liao, et al. [35] un-
derline the ‘age of the building’as a signicant obstacle to energy ef-
ciency and highlight the economic and environmental benets of
refurbishing buildings. This study considers a building age ranging from
0 to 100 to satisfy the linear interpolation criteria while also contem-
plating the decline in efciency with age. Matthew [36] states that
MOBs reach the end of economic life by 80 years unless retrotted for
energy efciency, reiterating the decline in energy efciency in older
buildings. Furthermore, 90 % of Australian MOBs are within 100 years
of age [36], which broadens the practicality of this study. These ndings
complement our analysis demonstrated in Fig. 2, which illustrates the
age distribution of MOBs in Melbourne based on the data from the
Building Information Dataset [37]., which shows that less than 20 % of
all MOBs exceed 100 years of age. However, for regional contexts where
the typical lifespans of MOBs may extend beyond 100 years, the scoring
can be modied accordingly with similar endpoints. The study con-
ducted by Sasso, et al. [38] is also referenced, which classies buildings
by their construction periods to analyse space heating demands.
3.2.2. Common property ownership
In MOB, the OCs shoulder the responsibility for administering and
maintaining the common properties. While some MOBs possess single-
tier ownership, granting the residents direct ownership over the CP,
complex MOBs may feature both ‘limited’and ‘unlimited’OCs, leading
to multi-tier ownership. The ‘limited OC’manages and owns distinct CPs
while enjoying exclusive benets and responsibilities, while the ‘un-
limited’OCs oversee the management of each CP [3], enabling them to
utilise the energy derived from a limited CP. This typology factor in-
corporates single and multi-tier OC ownership scenarios, accounting for
diverse energy allocation considerations.
The inuence of CP ownership in energy allocation is seldom
explored in the literature and represents a novel aspect of this study.
However, the effect of CP ownership on the other attributes, such as cost
allocation, is evident. For instance, the residents without access to a
particular CP often require by-laws to benet from the property, such as
parking spaces allocated to a limited OC [39]. Similarly, by-laws are
Fig. 2. Age distribution of MOBs in Melbourne.
A. Poshnath et al.
Energy & Buildings 320 (2024) 114601
5
necessary to allow all residents to use the electricity generated by a
limited OC-owned solar panel, which may sprout disputes due to dis-
agreements. Legislative records from Victoria, Australia, evidence these
challenges where expenses incurred by a particular OC were being paid
from the contributions of a different OC[40]. Thus, consideration of CP
ownership is crucial in allocating commonly shared resources, which is
often overlooked.
3.2.3. Tenant percentage
MOBs are expected to have a higher percentage of tenants residing in
the building, particularly in cities. However, rental properties can
hinder the household energy transition [41]. Hammerle, et al. [41] have
identied affordability, long payback periods, split incentives, poor
savings, and lack of government support as the main barriers to adopting
RES. Moreover, the higher turnover rate in MOBs augments the issue
[42]. This typology factor analyses the potential inuence of the tenant
population on the decision-making processes. The most suitable energy
allocation model for MOBs is determined by categorising tenant pop-
ulations as signicant (greater than 70 % tenant share), moderate (from
31 % to 70 % tenant share) and inferior (less than or equal to 30 %
tenant share).
3.2.4. Building height
The spatial constraints intensied the momentum for vertical de-
velopments in urban areas, particularly in high-rise MOBs. Factors such
as the building’s height, shape and utility inuence the energy demand
across the units [43]. The higher the building, the higher the energy
demand for common services such as elevators, which can lead to dis-
putes over energy cost allocation. Moreover, the electricity consumption
of apartment units may vary signicantly with respect to the storey [44].
Godoy-Shimizu, et al. [45] correlated the energy consumption and
height of ofce buildings, exposing a 137 % increase in energy con-
sumption for buildings beyond ve storeys. Conversely, higher-oor
units are exposed to external temperature variation more frequently
than lower oors, resulting in increased energy demand, not merely
inuenced by lifestyle or personal choices. This typology factor adopts a
modied version of the height-based building classication recom-
mended by the ABS [46], grouped as 1 to 3 storeys, 4 to 8 storeys, and
greater than eight storeys for incorporating the variation of the suit-
ability of energy allocation models with a building’s height.
3.3. Proposed energy allocation models
The energy allocation models practised in MOBs are relatively
inconspicuous and follow one of the few established systems of common
cost allocation. However, most of these allocation systems prove un-
suitable for energy allocation due to the intrinsic mismatch of objectives.
This section comprehensively scrutinises the commonly practised and
hypothetical potential allocation models, contributing to the novelty of
the research. The attributes of energy allocation models are analysed
based on the assumption that renewable energy is allocated during the
day since the model does not consider the presence of batteries. Table 1
briey outlines the denition of each energy allocation model consid-
ered in the study.
3.4. Suitability analysis
The energy allocation models possess inherent characteristics that
may alter their suitability according to building typology. Several at-
tempts have been made to develop suitability matrices in various
research dimensions, such as material science [47] and land adminis-
tration [48]. This section scrutinises the suitability of the allocation
models based on the building typology factors outlined in Section 3.2.,
encompassing building age, common property ownership structure,
tenant composition and building height. The suitability scales are
developed assuming the stakeholders prioritise fair energy allocation
Table 1
Denition of Evaluated Energy Allocation Models.
Energy
Allocation
Models
Denition Pros Cons
Equal
Allocation
The generated
energy is equally
distributed among
the apartment units
irrespective of the
energy demand of
each unit
Easy to implement
Suitable for
apartments with
similar
characteristics
Inequitable allocation
Challenging to achieve
a net-zero target due to
disparity
Fails to capture the
apartment
characteristics
Roof
delineation
The roof area is
delineated for each
unit based on their
ownership
percentage for
individual
installation of RES
Individuals can
install their own RES
of desired capacity
Since the roof is owned
by everyone, collective
consent should be
procured
The responsibility of
roof maintenance
should be clearly laid
out
Individual installation
of accessories may lead
to more space
utilisation
Few apartments could
be vulnerable to over-
shadowed areas
Floor-area The generated
energy is distributed
based on the oor
area percentage of
each apartment unit
Indicator of energy
consumption
Static quantity
Apartments with the
same oor area may
have different energy
demands due to
external factors such
as orientation and
storey height
Occupant
Count
The generated
energy is allocated
based on the number
of occupants in the
unit.
An indicator of the
energy demand of
the unit
Potential disparity as
lifestyle choices may
inuence energy
consumption
irrespective of the
occupancy
Hard to monitor the
occupancy
Frequent recalculation
required
Flat fee A xed fee is
charged irrespective
of the consumption
Easy to implement
Monitoring is not
required
Detrimental to net-zero
goals
Leads to over over-
utilisation of energy
and disparity
Electricity
Demand
The generated
energy is allocated
based on the energy
demand of the
apartment unit
Direct indicator of
energy demand
Easy to incentivise
those who use less
energy
Need to set the period
for standardisation
Could lead to potential
wastage if no limit is
set, as people who use
more energy will get
more benets
Auction The users can bid for
the energy they
need, with an option
to abstain
Not inuenced by
usage or apartment
characteristics
Enables high energy
users to bid
according to their
needs while allowing
abstention
Mandates heavy
administrative
workload
Need to set bid limit to
ensure fairness
Investment The energy
generated is
distributed based on
the share of
investment each unit
has contributed,
without abstention
Provides an
opportunity to invest
only in the energy
required
Mandatory
participation
Should set limits on
investment to ensure
fairness
Lot
entitlement
The generated
energy is allotted
based on the lot
entitlement of each
unit
Backed by law
Common practice
for allocation of
common costs
Reects the market
value of the property
Challenging to achieve
a net-zero target due to
the disparity
A. Poshnath et al.
Energy & Buildings 320 (2024) 114601
6
models while accounting for individual ownership of the common
property and minimising personal disadvantages. Though numerous
notations represent suitability [49,50], this methodology engages a 3-
point scale denoted as ‘high’, ‘medium’and ‘low’to represent the suit-
ability ratings. The suitability scales adopted for each building typology
factor, the relevant assumptions made for the analysis, the justication
for the assessment and the qualitative categorisation as per the three-
point scale have been detailed in Appendix A. Table 2 to Table 5 tabu-
lates the qualitative suitability classication of building typology pa-
rameters adopted in the study.
3.5. Impact analysis
Despite the diverse suitability across building typology, the alloca-
tion models carry an inherent risk of sprouting disputes. However, due
to limited resources addressing the likelihood of such risk occurrences,
this section conducts an impact assessment of the allocation models,
considering the nancial and operational aspects and the cost of inac-
tion. The nancial domain scrutinises the impact on the cost implica-
tions of adopting the allocation model and the nancial disparity,
detailed in APPENDIX B. Concurrently, the operational domain analyses
the impact of implementation and management of the allocation
models.
A 20 kW solar panel with a generation potential of 120kWh is
considered in the numerical modelling to simulate the nature of the cost
incurred due to inaction and nancial disparity. Key assumptions and
the probability density function for each allocation model, such as the
oor area of the building, energy demand prole, occupancy, lot enti-
tlement, at fee, auction and investment, are detailed in APPENDIX B.
The simulations revealed that equal allocation results in the least
nancial disparity among apartment owners, while more dynamic
models such as at fee, auction, and investment-based allocations dis-
played the highest disparity, highlighting increased user-centricity and
greater inequality. Conversely, the cost of inaction is higher for the static
allocation models, such as equal and oor area-based allocation, indi-
cating the potential savings associated with these models.
The impact rating ranges from 1 to 5, with 1 being the model with the
least negative impact. The rubric for the impact analysis can be seen in
APPENDIX B, and the impact rating and nal impact score of each
allocation model are demonstrated in Table 6. Though the ease of
implementation also inuences the suitability scores, the operational
impact score underscores the general characteristics of the allocation
model, which may remain consistent across the building typologies.
Ultimately, the impact score of each allocation model is aggregated
using Equation (2). The Euclidean distance pattern –a well-adopted
technique for aggregating multiple factors into a single metric [51]–is
employed to dene the impact score. Incorporating the square root of
the sum of squares balances the inuence of each parameter, negating
the outliers [52]. Including a cubic root for the sum of three distinct
parameters moderates the aggregated impact. Similar approaches have
been adopted in [53], where the formula incorporates the geometric
mean, balancing the effect of outliers in real-life scenarios while using
the normalisation approach to balance the inuence of different impact
parameters.
3.6. Decision-Making approach
Subsequent to the generation of suitability and impact scores, both
are amalgamated to arrive at a composite score through multiplication,
constituting the rst step towards decision-making. Since the allocation
model with the higher suitability ratings indicates better performance,
while the higher impact score indicates the potential drawbacks, the
nal scoring is evaluated using Equation (3).
Composite Score =Suitability Score × (5−Impact Score)(3)
Table 2
Qualitative Suitability Classication Based on Building Age.
Energy Allocation Models Old New
Equal Allocation Medium Low
Roof Delineation Low Medium
Floor-Area High Medium
Occupant Count Low Medium
Flat Fee Low Low
Energy Demand High High
Auction High High
Investment High High
Lot Entitlement High Medium
Table 3
Qualitative Suitability Classication based on CP Ownership Structure.
Energy Allocation Models Single Ownership Multiple Ownership
Equal Allocation High Low
Roof Delineation Medium Low
Floor-Area High Medium
Occupant Count High Low
Flat Fee Medium Low
Energy Demand High Medium
Auction High Medium
Investment High Medium
Lot Entitlement High Low
Table 4
Qualitative Suitability Classication Based on Tenant Percentage.
Energy Allocation
Models
Signicant (>70
%)
Moderate (31–70
%)
Inferior (<=30
%)
Equal Allocation High Medium Low
Roof Delineation Low Low Medium
Floor-Area High High Medium
Occupant Count Low Low Medium
Flat Fee Medium Low Low
Energy Demand Low Medium High
Auction Low Medium High
Investment Low Low High
Lot Entitlement High High Medium
Table 5
Qualitative Suitability Classication Based on Building Height.
Allocation Model High-rise (100 to 9) Mid-rise (4 to 8) Low-rise (<=3)
Equal Allocation Low Medium High
Roof Delineation Low Low Medium
Floor-Area Low Medium High
Occupant Count Medium High High
Flat Fee Medium Low Low
Energy Demand High High High
Auction High High High
Investment High High High
Lot Entitlement Low Low Medium
Impact Score = (
cost implications2+financial disparity2
√+
implementation challenge2+management challenge2
√+ (cost of inaction))1
3(2)
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To rene the selection process, the TOPSIS analysis is employed to
determine the most suitable energy allocation model by analysing the
model closest to the ideal solution. This approach accommodates the
variations in building characteristics and subjective preferences of OCs,
offering the exibility to assign the weightage for each category, which
will serve as input for the TOPSIS analysis. The analysis provides a
ranked order of allocation models, offering the stakeholders the data-
backed evidence to facilitate informed decision-making.
Despite being the highly ranked allocation model for the building
typology, its suitability in achieving the overarching objectives, such as
the net-zero electricity goal, is uncertain. Thus, a proximity analysis of
the highly ranked allocation models is recommended to ensure they
serve the dual purpose of being suitable for the building typology while
attaining the broader objectives for which the RES is installed. The
approach to the proximity analysis is context-dependent and hence may
prohibit universal application, suggesting to perform an independent
analysis. Hence, it is recommended that the OC or the concerned
stakeholder perform a thorough performance analysis to achieve the
specic objective. Section 4 demonstrates the methodology for
achieving net-zero electricity status in a MOB.
4. Demonstration
The efcacy of each allocation model is intricately linked with the
typology of the MOB. The framework elucidated in Section 3.1 is used to
evaluate the appropriateness of each allocation model for two distinct
building typologies, as outlined in Table 7. As illustrated in Fig. 3, the
performance among energy allocation models differs vastly in each ty-
pology, underlining the signicance of building typology factors in
choosing the most suitable energy allocation model.
In Fig. 3, building Typology 2 scores highly across various building
typology factors, representing the older building with single ownership
and fewer tenants. Conversely, Typology 1 underperforms in terms of
building age and CP ownership, outperforming Typology 2 solely in
terms of tenant percentage. Notably, equal allocation may be better
suited for buildings with a higher tenant percentage (65 % as per
Table 7) while unsuitable for newly constructed buildings. Similarly,
roof delineation is inappropriate for RES in buildings with multiple
ownerships but viable for older buildings with single ownership. Floor
area-based allocation largely favours older, mid-rise buildings, while
occupancy-based allocation benets newer buildings.
The performance of the at-fee model is dismal across the typologies,
indicating a high dispute potential. Typology 2 demonstrates superiority
over Typology 1 across the parameters in electricity demand-based,
auction-based and investment-based allocations, highlighting their
effectiveness in regional suburbs where such building typologies are
prevalent. Similarly, if single ownership is adopted, lot entitlement
emerges as a potential model in newer buildings with a higher tenant
percentage, similar to the city dwellings. These graphical representa-
tions enable the visual comprehension of the holistic performance of
energy allocation models regarding the building and each typology
factor at a granular level.
Capturing the involatile traits of the energy allocation models
regardless of the building typology, the impacts of adopting the energy
allocation models are represented through the composite scores, illus-
trated in Fig. 4, offering more profound insights into the performance
variation of the energy allocation models. For instance, energy alloca-
tion based on electricity demand demonstrated high suitability for
building typology 1, while investment-based allocation emerged as the
most suitable model for building typology 2. Equal and lot entitlement-
based allocation demonstrated a signicant variation across the pa-
rameters in both typologies. Most allocation models scored lowest in
multiple ownership scenarios, except for the electricity demand, in-
vestment and auction-based models, where a higher tenant proportion
affected their overall suitability. However, for typology 2, these models
showed a balanced outcome exhibiting similar suitability across the
building characteristics. The detailed calculations supporting the per-
formance assessment are narrated in APPENDIX C. This comprehensive
analysis elucidates the symbiotic relationship between the energy allo-
cation models and building typology.
Subsequent to the composite scoring, the TOPSIS analysis is per-
formed to identify the energy allocation models closest to the ideal so-
lution, attaining the maximum score. The framework is designed with
the exibility to adjust the weights for each typology factor to accom-
modate the subjective notions of the OCs. Table 8 presents the assumed
weights of typology factors for demonstration purposes. The ranked
order of the energy allocation models, post TOPSIS analysis, is listed in
Table 9 for both typologies. Notably, the ranking of energy allocation
models differs for both typologies, underscoring the necessity for a
tailored approach based on building characteristics. This differentiation
spotlights the necessity to carefully consider the building-specic factors
while selecting the suitable energy allocation model.
The ranking of energy allocation models obtained through the
TOPSIS analysis is tailored to a building typology; however, com-
plementing each MOB may have specic objectives for adopting RES,
such as attaining net-zero energy status, and a granular level assessment
is essential. The nal phase of the framework aims to assess the per-
formance of top-ranked energy allocation models for attaining these
objectives. APPENDIX C demonstrates an objective proximity analysis
for attaining net-zero electricity in the discussed building typologies.
The three highly performing energy allocation models from the TOPSIS
analysis were chosen for their objective proximity analysis, adding the
Table 6
Impact Analysis of Energy Allocation Models.
Energy Allocation Models Impact Ratings Impact Score
Financial Impact Operational Impact Cost of Inaction
Cost Implications Financial Disparity Implementation Management
Equal Allocation 1 1 1 1 5 1.9
Roof Delineation 4 5 5 5 1 2.4
Floor-Area 2 3 2 1 4 2.1
Occupant Count 2 3 3 2 4 2.2
Flat Fee 1 5 1 1 3 2.1
Electricity Demand 3 3 3 3 5 2.4
Auction 5 5 4 1 3 2.4
Investment 5 5 2 1 3 2.3
Lot Entitlement 2 4 2 2 3 2.2
Table 7
Building Typology Inputs.
Inputs Typology 1 Typology 2
Age of Building 5 years 70 years
CP Ownership Multiple Ownership Single Ownership
Tenant % 65 12
Building Height (Storeys) 12 4
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Energy & Buildings 320 (2024) 114601
8
dimension of both typology-dependent and objective-dependent nature
of the allocation models. However, stakeholders possess the discretion to
choose the number of allocation models for the evaluation. Remarkably,
the ranking based on TOPSIS and objective proximity analysis often
diverges, as demonstrated in Table 10.
The electricity demand-based allocation model demonstrated supe-
rior performance for Typology 1 in both TOPSIS and objective-proximity
ranking, indicating its robust suitability while aiding Typology 2 to
attain net-zero electricity status. Conversely, while investment-based
allocation displayed better performance for Typology 2, it may not
favour achieving the net-zero. Overall, autonomous models like auction
and investment-based allocation models perform better in TOPSIS
analysis, while the demand-responsive allocation model, like the elec-
tricity demand, may better suit the attainment of net-zero. However,
these results could vary widely among the building with respect to their
typology characteristics.
The Typology 1 building, which can be characterised as a newly
constructed, tall MOB in urban areas with an overwhelming tenant
proportion of 65 %, necessitates a demand-responsive model, such as the
electricity demand-based allocation to suit its building characteristics
and achieve net-zero objectives. Interestingly, the objective-proximity
ranking for net-zero status closely aligns with the TOPSIS ranking,
allowing stakeholders to select their preferred model, irrespective of the
analysis.
Conversely, Typology 2, characterised as an older MOB located in the
regional suburbs with a higher proportion of owner-occupiers, revealed
more variation in results. Increased owner-occupancy in the MOB may
bring diverse preferences, as these residents have a long-term stake and
greater vote share in decision-making. Contextually, the investment-
based allocation model, which was not a preferred choice for Typol-
ogy 1, emerged as the most suitable model based on the building ty-
pology factors. However, the electricity demand model ranked highest
for net-zero objectives, followed by the investment model, where OCs
may have to prioritise their objective. Notably, all preferred models in
Typology 2 were user-centric, allocating the energy based on the user’s
needs and behaviour, contrasting with Typology 1, where static allo-
cation models such as oor area-based allocation could have been
considered.
The preference divergence between Typology 1 and Typology 2
could be attributed to the impact of tenant proportion in decision-
making power. However, it is essential to recognise that choosing
electricity-demand-based allocation for newer city MOBs and
Fig. 3. Disparity of Suitability Scores Across Building Typologies Among Energy Allocation Models.
A. Poshnath et al.
Energy & Buildings 320 (2024) 114601
9
investment-based allocation for older regional MOBs is not universally
applicable. The equal weights assigned for the Typology 1 building may
have contributed towards the consistent results across TOPSIS and
Fig. 4. Variation of Composite Scores Across Building Typologies.
Table 8
Assigned Weights for each Typology.
Typology Factors Typology 1 Typology 2
Age 25 30
CP Ownership 25 15
Renter % 25 25
Storeys 25 30
Table 9
Ranked Order of Energy Allocation Models.
Energy Allocation Models Typology 1 Typology 2
Equal Allocation 6 7
Roof Delineation 8 8
Floor-Area 3 4
Occupant Count 7 6
Flat Fee 9 9
Electricity Demand 1 2
Auction 2 3
Investment 4 1
Lot Entitlement 5 5
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Energy & Buildings 320 (2024) 114601
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objective-proximity analysis, whereas the differing weights to building
parameters for Typology 2 are representative of the subjective prefer-
ences of the OCs. Moreover, the objectives behind RES installation are
also subjective to the OC, ranging from attaining net-zero status to
obtaining policy benets, social benets, or the enhancement of the
well-being of the residents; each demanding a dedicated objective
proximity analysis.
The variation between TOPSIS and objective-proximity analysis re-
sults inherently arises from the differing denitions of both approaches.
In TOPSIS analysis, the ideal solution is to attain the highest suitability
score, while for the objective proximity analysis, the goal is to attain the
net-zero electricity status, as shown in APPENDIX C. Moreover, the
TOPSIS analysis considers a particular building typology dened by age,
ownership type of CP, tenant proportion, and building height. However,
even buildings with the same typology may have different objectives for
installation of RES, for which the granular level analysis of apartment
characteristics such as the energy demand, the occupancy and the oor
area of each apartment unit could be critical. This nuanced approach
emphasises the signicance of performing an objective proximity anal-
ysis to warrant that the chosen models t the building’s typology and the
specic energy goals, which adds to the novelty of this work.
5. Discussions
The menial adoption of RES in MOBs substantially threatens the
transition to renewable energy and the attainment of net-zero objec-
tives. The ownership conundrum of the jointly-owned RES and the lack
of clarity regarding the allocation of renewable energy among the
apartment owners further deter the RES uptake in the common prop-
erties of MOBs. The limited literature and legislative support for energy
allocation only exacerbate the problem. In this study, we spotlight the
underexplored realm of renewable energy allocation within MOBs and
contemplate the characteristics of potential energy allocation models.
The study introduces an unparalleled evidence-based decision-making
framework to identify the most suitable energy allocation model for
each unique MOB.
The study introduces nine distinct energy allocation models for the
MOBs, considering variables such as MOB age, common property
ownership, tenant proportion and building height. This unique amal-
gamation of a MOB’s physical and social characteristics is novel in the
eld of renewable energy allocation. For instance, older buildings may
not adhere to the latest energy efciency standards, which inuences the
suitability of the proposed energy allocation models. Moreover, the
complex ownership structure of the common property can potentially
lead to disputes if the guidelines concerning energy allocation are
inadequately dened. The higher turnover rate of tenants in MOB could
disrupt the collective decision-making process, while the residents in
higher storeys may be disadvantaged in energy usage due to external
atmospheric conditions. Addressing these complex dynamics, the pro-
posed framework conducts an in-depth evaluation of the building ty-
pology to assess the suitability of each energy allocation model,
fostering the adoption of RES in MOBs.
The suitability assessment of the energy allocation models is sub-
jective to factors ranging from building characteristics to the country’s
legislation. While the discussion primarily focuses on the Australian
context, the framework is adaptable irrespective of the region, with
necessary modications to the suitability scales. Additionally, the
weightage assignment of building typology factors is aimed to account
for the inuence of subjective notions of the owners’corporations.
The framework attains robustness through the comprehensive
impact analysis of each energy allocation model across the nancial and
operational domains, which is typology-independent. Adopting Monte-
Carlo simulation widens the opportunity to make the impact scores
more consistent and reliable by simulating numerous scenarios, effec-
tively addressing the cost of inaction. The suitability and impact scores
are amalgamated to derive a quantitative score that could persuade
stakeholders of the most suitable energy allocation model. The TOPSIS
analysis further enhances the assessment by providing a consistent
ranked order of models while allowing stakeholders to modify each ty-
pology factor’s importance based on their inclinations. The key ndings
of the analysis of the energy allocation models are highlighted in the
following discussions.
a) The suitability of each energy allocation model is heavily intertwined
with the building typology, as evident from Section 3. Consequently,
generalising a particular energy allocation model, akin to allocating
common expenses, may not yield a favourable outcome and could
escalate disputes.
b) The energy demand across the apartments within the MOB may vary
due to factors beyond the residents’control, such as exposure to
direct sunlight, highlighting the crucial role of equitable energy
allocation.
c) Despite the benets, the energy allocation models possess inherent
risks. As evident from Table 6, equal energy allocation will result in a
minor nancial disparity, while roof delineation may be the efcient
approach for negating the cost of inaction. Be that as it may, these
allocation models may possess other innate risks affected by the
typology-dependent factors −affecting their overall suitability. This
spotlights the necessity to integrate both the typology-dependent
suitability and the typology-independent impacts for the analysis
of the overall suitability of the allocation models.
d) The TOPSIS serves as a valuable tool in identifying the energy allo-
cation model closest to the positive ideal solution, attaining the
maximum score by providing a ranked list of the models. However,
the ranking should not be strictly observed as a ‘best to worst’hi-
erarchy but as a spectrum of choices aiding the MOBs in making an
evidence-based decision on the preferred energy allocation model,
according to the discretion of the stakeholders.
e) The MOBs may have different objectives for installing RES, ranging
from net-zero status to welfare maximisation of residents. A partic-
ular energy allocation model may excel in achieving an objective
despite not being the top-ranked option recommended by TOPSIS, as
indicated in Table 10. Therefore, the framework recommends per-
forming a nal objective-proximity analysis to identify the best en-
ergy allocation model for the MOB, warranting the avour of both
being suitable for the MOB and the adopted objective.
The study presents an innovative approach for choosing the energy
allocation model catering to the MOBs, which is the rst of its kind to the
best of our knowledge. The study analyses the characteristics of nine
distinct energy allocation models, some unique in the existing discussion
on energy allocation models. Despite the methodology being novel, the
analysis of allocation models is grounded on the tested theories of
TOPSIS, augmenting the validity of the results obtained through the
framework. The consolidation of both social and physical characteristics
of the MOB, the incorporation of suitability and impact analysis for the
attainment of composite scores, and the objective proximity analysis
under the umbrella of an evidence-based decision-making approach
Table 10
Comparative Ranking from TOPSIS and Objective-Proximity Analysis.
Energy
Allocation
Models
Typology 1 Typology 2
TOPSIS
Ranking
Objective-
Proximity
Ranking
TOPSIS
Ranking
Objective-
Proximity
Ranking
Floor Area 3 2 − −
Electricity
Demand
1 1 2 1
Auction 2 3 3 3
Investment − − 1 2
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Energy & Buildings 320 (2024) 114601
11
contribute to the study’s novelty.
Despite the introduction of technologies facilitating the distribution
of commonly generated energy to each apartment unit, the reliance on
inequitable energy allocation models has been a concern. The frame-
work is poised to guide the OCs, the RES installers, and other relevant
stakeholders to make well-informed decisions to aid in the smooth
transition towards renewable energy, ultimately contributing to the
overarching environmental goals. Allocating energy entitlement
through this approach is expected to create a sense of ownership among
the residents and promote responsible energy consumption. Moreover,
policymakers can identify the most suitable energy allocation models for
a particular region, leveraging the dominance of similar building
structures and assisting in providing targeted incentives and legislative
support for incrementing the adoption rate of RES. Furthermore, the
research exposes new avenues for spatial-based energy planning,
enabling policymakers to enhance sustainability initiatives.
The framework thoughtfully addresses a broad spectrum of factors
that may potentially affect renewable energy adoption, ranging from the
turnover rate of apartments to the operational impacts of elevators.
However, the framework could be made more efcient by integrating
additional factors, such as the apartment orientation and shadings, that
may directly or indirectly inuence energy allocation. Moreover, the
study involves assumptions such as the decline in energy efciency with
the age of the building that might not hold universally. The suitability
scales in Appendix A carry a degree of subjectivity and may vary
regionally. Moreover, a broader range of typology factors may enhance
the framework’s robustness. Be that as it may, the framework offers the
exibility and adaptability to assess suitability based on regional nu-
ances or subjective preferences while offering a consistent and reliable
underlying methodology for making informed choices.
6. Conclusion
The suboptimal adoption rate of Renewable Energy Systems (RES) in
Multi-Owned Buildings (MOBs) underscores the vacuum of research on
the energy allocation models. The study addresses this void by intro-
ducing a comprehensive methodology for selecting the most suitable
energy allocation model for a building typology through an evidence-
based decision-making approach to assist the stakeholders and policy-
makers in making well-informed decisions regarding RES adoption in
MOBs.
The methodology systematically integrates a MOB’s physical and
social characteristics, employing TOPSIS analysis to identify the most
suitable energy allocation model. The study performs a comprehensive
analysis of the parameters of energy allocation models that are inu-
enced by the building typology and those unaffected by it, contributing
to a holistic understanding of their performance. Moreover, the study
acknowledges that the most suitable energy allocation for a specic
building typology may not necessarily align with the RES installation’s
overarching objective. Therefore, the study recommends a concluding
objective proximity analysis, contributing a dimension that considers
both the suitability for the building typology and the installation
objective.
The study challenges the prevailing ‘one-size-ts-all’policy
approach and advocates for a targeted, regulated, and adaptive
approach that contemplates diverse building typologies. This approach
offers critical insights into developing regulatory measures or targeted
incentives and underscores the necessity of legislative support and
standardised regulations to entrust the owners’corporations to adopt
RES. The framework substantiates each apartment unit’s energy enti-
tlement, thereby fostering a sense of ownership. The framework can also
be expanded to introduce a ‘penalisation-incentivisation’approach for
consumers, promoting responsible energy usage. Moreover, the frame-
work shall be adopted irrespective of the nation of interest, with
necessary modications to the suitability scale.
Being novel, the framework’s robustness could be enhanced by
considering a broader range of building typology factors, such as the
orientation of the building, insulation quality, and regional climate
factors. The research can also benet from analysing more potential
stand-alone or hybrid energy allocation models. Considering a ‘Time-of
Use’based allocation methodology could leverage the practicality of the
study across the various energy systems. Though the current framework
is developed for residential MOBs, the concept can include mixed-use
MOBs, commercial establishments, or community-owned RES with
suitable modications. This expansion allows opportunities to collabo-
rate with industry partners and policymakers to pilot the framework in
multiple scenarios.
Additionally, developing regulatory guidelines of energy allocation
could assist in dispute resolutions. Integrating Industry 4.0 technologies
such as ‘Digital Twin’and ‘Internet of Things’to aid in the visualisation
and real-time monitoring of energy allocation may assist in regulating
the models and augmenting their robustness. Moreover, applying the
framework to spatial-based energy planning extends its use to city-level
analyses, allowing regional evaluations to interpret the inuence of
demography on energy allocation. These developments would substan-
tially progress the innovation of more efcient and equitable energy
allocation systems.
CRediT authorship contribution statement
Aravind Poshnath: Writing –original draft, Visualization, Meth-
odology, Investigation, Formal analysis, Data curation, Conceptualiza-
tion. Behzad Rismanchi: Writing –review &editing, Validation,
Project administration, Funding acquisition, Conceptualization. Abbas
Rajabifard: Writing –review &editing, Supervision, Conceptualization.
Declaration of competing interest
The authors declare that they have no known competing nancial
interests or personal relationships that could have appeared to inuence
the work reported in this paper.
Data availability
No data was used for the research described in the article.
Appendix A
The suitability of the energy allocation models is intertwined with the building typology under consideration. This section details the suitability
scales adopted for each building typology factor, ranging from the age of the building, ownership structure of the CP, percentage of tenants in the MOB
and the building height, accompanied by the assumptions considered for arriving at the qualitative classication, supported by the denition of the
suitability rating considered for each typology factor, and the procedure followed for the quantitative classication.
A. Suitability analysis of building age
The ageing building stock remains a signicant obstacle to the energy transition due to lower energy efciency and non-adherence to modern
A. Poshnath et al.
Energy & Buildings 320 (2024) 114601
12
energy standards. For this study, several assumptions underline the suitability analysis correlated with the age of MOB:
1. Energy efciency declines as the age of the building increases
2. Assessment of energy efciency of individual units in older buildings is complicated
3. Contemporary buildings adhere to the evolution of technology and energy efciency standards
This typology factor incorporates building age as a numerical value between 0 and 100 years, with lower values representing newer buildings. The
suitability score is calculated in three stages:
1. Qualitative categorisation of MOB as high, medium and low according to Table A.1., with quantitative scoring of 100, 50 and 0, respectively, for
the classications represented in A.2.
2. Denition of upper bound and lower bound scores for each allocation model according to the qualitative suitability range
3. Calculation of the suitability score using the linear scaling method, which may vary within the upper and lower bound range
Table A1
Suitability Ratings Based on Building Age.
Suitability
Rating
Denition
High The allocation model is highly suitable, with minimal modications to achieve substantial energy savings
Medium The allocation model provides moderate benets, particularly when energy efciency cannot be quantied precisely. Disputes might arise where quantication does not directly
correspond to energy efciency
Low Not suitable for increasing energy efciency, demanding signicant administrative efforts or being cost-ineffective
Table A2
Suitability Scale for Building Age.
Allocation
Model
Old Justication New Justication
Equal
Allocation
Medium Easy adaptability: Users may nd it easy to adapt to the model when the
energy efciency of individual apartments cannot be quantied
Inequitable allocation: Inequitable allocation may result in disputes
among the apartment owners
Low Inequitable allocation: Though new buildings may adhere to a
standardised energy efciency rating, equal allocation may lead to
inequitable allocation, especially where the individual apartment
efciencies are measurable.
Differing apartment characteristics: Does not account for the apartments of
varying sizes and characteristics
Roof
Delineation
Low Require retrots: May necessitate heavy retrots for an old building,
which an apartment owner may have to bear individually, including
repairs liability.
Medium Adaptability: A new building may have the provisions for installation of
RES, with minimal retrots
Limitations on future expansions: However, future expansions will be
limited as new buildings are supposed to serve long-term needs
Floor-Area High Easy adaptability: Users may nd it easy to adapt to the model when the
energy efciency of individual apartments cannot be quantied
Consistency: Frequent recalculation of entitlement is not necessary since
the oor area is static
Reects energy demand: Floor area is an indicator of energy demand,
making it suitable where the energy efciency of individual units cannot
be quantied
Medium Variability in energy efciency: The energy efciency of individual
apartments with the same oor area may vary based on factors such as
window orientation and storey height, leading to potential inequitable
allocation
Consistency: Frequent recalculation of entitlement is not necessary since
the oor area is static, and there is less probability of disputes
Occupant
Count
Low Difculty in monitoring occupancy pattern: May not possess the
technical capability to monitor occupancy pattern
Difculty in relating demand changes to occupancy: Difcult to
ascertain if any increase in demand is due to an increase in occupancy
Medium Better facilities to maintain occupancy data: Improved systems could be in
place to record the occupancy
Higher turnover rate: The turnover rate of apartments in new buildings is
higher compared to old buildings, necessitating the need for recalculation
of entitlement frequently
Flat Fee Low Potential for over-exploitation: May lead to excessive consumption of
electricity and prove disastrous for old buildings with low energy
efciency
Low Potential for over-exploitation: May lead to excessive consumption of
electricity with little incentive for energy conservation, disregarding the
energy efciency standards met.
Electricity
Demand
High Reective of energy usage: Indicator of the energy efciency of the
apartment.
High Suitable for allocating common property usage cost: Fair allocation model
when individual energy consumption of common properties is also
considered
Auction High Equitable allocation: Fair and equitable process where those who
consume less energy must pay only for what they need.
Not inuenced by the age of the building: The model is least inuenced
by the age of the building, though it can contribute to improving overall
efciency by enabling the installation of RES
High Equitable allocation: Fair and equitable process where those who consume
less energy need to pay only for what they need.
Not inuenced by the age of the building: The model is least inuenced by
the age of the building, though it can contribute to improving overall
efciency by enabling the installation of RES
Investment High Not inuenced by the age of the building: The model is least inuenced
by the age of the building, though it can contribute to improving overall
efciency by enabling the installation of RES
High Not inuenced by the age of the building: The model is least inuenced by
the age of the building, though it can contribute to improving overall
efciency by enabling the installation of RES
(continued on next page)
A. Poshnath et al.
Energy & Buildings 320 (2024) 114601
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Table A2 (continued )
Allocation
Model
Old Justication New Justication
Sense of ownership: Can provide a sense of ownership to the residents,
which eventually encourages the users to invest in energy-efcient
practices
Possibility for extra revenue: Though the apartments could already be
energy efcient, investment-based allocation provides an opportunity for
new building residents to earn extra revenue through peer-to-peer trading,
with minimal modications/retrots
Lot
Entitlement
High Accustomed practice: Old buildings have practised allocation of common
cost based on lot entitlement
Uncomplicated space ownership: Old buildings may not include complex
3D shapes to sprout issues related to area ownership
Medium Complicated ownership: Lot entitlement is complex to calculate and differs
on various aspects, such as the market value and rentable area of the
apartment and the ownership of 3D spaces that are common in new
apartment buildings.
Legally supported: Lot entitlement possesses legislative backup for
performance that helps in minimising disputes
B. Suitability analysis of common property ownership
The ownership of the CP is classied as single and multiple, recognising the presence of ‘unlimited’and ‘limited’CPs, respectively. This typology
factor assumes input as either ‘Single Ownership’or ‘Multiple Ownership’, encompassing the following assumptions.
1. The ownership share of common property varies across the units
2. In ‘single’ownership, each unit directly owns and manages the RES
3. In ‘multiple’ownership, all residents retain ownership of the common property; however, specic stakeholders possess exclusive privileges and
responsibilities
The suitability score is calculated in three steps:
Qualitative categorisation of both ownerships as high, medium and low following Table A.3., with quantitative scores of 100, 50 and 0 respectively
Denition of upper bound and lower bound scoring for each allocation model based on the qualitative suitability range provided in Table A.4.
Calculation of the quantitative suitability score of each allocation model based on input by matching against the qualitative scores.
Table A3
Suitability Ratings Based on CP Ownership Structure.
Suitability Rating Denition
High The model effectively accommodates the ownership scenarios and ensures fair energy allocation with the most minor disputes
Medium Although the model addresses specic concerns, there lies a potential for moderate disputes or complexities due to unfair distribution
Low High potential for ownership-related disputes, mandating extensive administrative intervention for resolution
Table A4
Suitability Scale for CP Ownership Structure.
Allocation
Model
Single
Ownership
Justication Multiple
Ownership
Justication
Equal
Allocation
High Fair energy allocation: Fair energy allocation as all residents draw
energy from the common property that they own
Low Require administrative intervention: Allocating equal energy to
those who do not own the RES may necessitate administrative
intervention.
Liability disputes: Disputes on the liability of RES and CP may also
arise, and the liabilities need to be mentioned
Roof
Delineation
Medium Administrative consent: Suitable when an individual has ownership
over the CP. However, since the CP is commonly owned, physical
delineation leads to administrative consent from a larger group of
people
Low Shared access with non-owners: Problem is exacerbated when non-
owner residents desire delineation of roof space
Excessive administrative intervention: Balancing the ownership and
sharing the benets with non-owners demands greater intervention
from the administration to minimise disputes
Floor-Area High Fair and equitable energy allocation: Equitable allocation is
expected as all residents are drawing energy from the common
property that they own
Medium Administrative intervention: Allocating the energy to those who do
not own the RES needs administrative intervention. However,
transparent guidelines help minimise disputes and administrative
intervention since the oor area is static
Occupant
Count
High Fair energy allocation: The occupancy pattern has a clear relation
with the energy demand of the apartment. Since all the members of
the apartment have ownership of the roof, disputes are expected to
be minimal.
Low Administrative intervention: Allocating the energy to non-owners
based on a variable typology such as occupancy needs extensive
administrative intervention and may be considered unjust to the
owners with a lower occupancy rate
Flat Fee Medium Inequitable allocation: Despite being easy to realise, disputes are
expected to sprout due to inequitable allocation
Low Lack of fairness and equity: Equal fees for both who owns and does
not own the CP with no check on electricity usage could prove
disastrous
Electricity
Demand
High Fair energy allocation: Since the energy is allocated based on the
demand prole of those who own the CP, disputes might be less from
the ownership perspective
Medium Administrative intervention: Allocating the energy to those who do
not own the RES necessitates administrative intervention
Potential for the disparity: Possibility of excessive usage by non-
owners
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Table A4 (continued )
Allocation
Model
Single
Ownership
Justication Multiple
Ownership
Justication
Auction High Equitable allocation: Suitable for single-tier ownership as everybody
is given a fair chance to bid for the energy they require
Medium Ownership independent: The process is independent of the
ownership of the CP
Priority bidding: May necessitate administrative requirements to set
the limit of purchase or similar agreements for non-owners
Investment High Equitable allocation: Suitable for single-tier ownership as everybody
is given a chance to invest in the energy they need
Medium Ownership independent: The process is independent of the
ownership of the CP
Maximise the potential tapping: Pooing the nancial resources of
non-owners may benet in installing RES of higher capacity and
efciency
Priority investment: May require a few administrative requirements
to set the limit of purchase or other agreements for the non-owners
Lot
Entitlement
High Fair and equitable energy allocation: Suitable for single-tier
ownership since all participating apartments have their distinct lot
entitlements
Low Administrative intervention: The responsibilities and privileges of
the common property are established through lot entitlement, which
owners possess. Allocating energy for the non-owners requires
extensive administrative intervention for laying down clear
guidelines regarding the responsibilities and privileges
C. Suitability analysis of tenant percentage
The percentage of tenants staying in a MOB plays a signicant role in arriving at a collective decision for energy transition, with higher tenant
percentages posing potential challenges. The tenant percentage is categorised as signicant (>70 %), moderate (31 to 70 %) and inferior (<=30 %) for
the analysis. The following assumptions are incorporated to arrive at the qualitative suitability scoring.
Tenants lack decision-making power unless granted by the owners of their apartments
Tenants tend to occupy the apartment for short-term
The qualitative scoring of the suitability of each allocation model is pre-dened following A.5., with corresponding upper and lower bound
quantitative scores ranging from 100 to 71 for ‘high’, 70 to 31 for ‘medium’and 30 to 0 for ‘low’. The input is a quantitative number denoting the
percentage of tenants in the building, which will be matched against the qualitative suitability classication given in Table A.6. The quantitative
suitability score is calculated by linear scoring ranging within the upper and lower bounds, except when the energy allocation models are independent
of the typology factor classication, ending up with a constant qualitative score. In such cases, the respective quantitative score is considered without
the linear score variation based on the tenant percentage.
Table A5
Suitability Ratings Based on Tenant Percentage.
Suitability Rating Denition
High The model is expected to give a fair outcome with minimal disputes/resolutions for the installation/operation of RES to both renters and owners
Medium The model is expected to produce moderate disputes but can be resolved with reasonable administrative inputs
Low The model produces inequitable or unfavourable outcomes for the stakeholders, possibly with frequent re-calculations
Table A6
Suitability Scale for Tenant Percentage.
Allocation Models Signicant
(>70 %)
Justication Moderate
(31–70 %)
Justication Inferior
(<=30
%)
Justication
Equal Allocation High Renters’Transient Nature: Does not
necessitate recalculation of
entitlement, though the tenure periods
of renters are short.
Lower population of owner-occupiers:
Owing to the lower representation of
owner-occupiers, equal allocation
eases the RES implementation with
the support of renters
Medium Renters’Transient Nature:
Considering the nominal share of
owner-occupiers, disputes are
expected due to inequitable
distribution
Low Signicant owner-occupier
population: Decision-making power
is skewed towards the owner-
occupiers.
Inequitable allocation: Long-term
investment with a consistent
occupancy pattern is expected, which
makes equitable energy allocation
necessary.
Roof Delineation Low Non-ownership of the roof by renters:
Inappropriate when the MOB has a
signicant renter population who do
not own the roof individually
Need for collective consent: Minor
percentage of owner-occupiers need
to collect the consent of owners of
rented apartments, who may not
enjoy the benets of RES installation.
Low Non-ownership of the roof by
renters: The MOB still has a
signicant renter population not
possessing individual ownership of
the roof
Need for collective consent: Minor
percentage of owner-occupiers need
to collect the consent of owners of
rented apartments, who may not
Medium Occupancy stability: Owner-
occupiers are expected to stay longer,
allowing for the outlining of clear
ownership and responsibilities of the
roof area.
Delineation disputes: A signicant
owner-occupier population may lead
to disputes over roof area
delineation.
(continued on next page)
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Table A6 (continued )
Allocation Models Signicant
(>70 %)
Justication Moderate
(31–70 %)
Justication Inferior
(<=30
%)
Justication
enjoy the benets of RES
installation.
Floor-Area High Stability in allocation: Being a static
measurement, the turnover rate of
renter apartments may not inuence
the energy allocation
High Equitable allocation for a balanced
population: Considering a
signicant renters population,
potential turnover rate and oor
area being a fair indicator of the
energy demand of apartments, the
allocation is expected to be more
equitable
Medium Potential inequity: Being a majority,
the occupier may demand a more
equitable allocation model that
captures more nuances of the
demand variability
Occupant Count Low Instability in occupancy: The
instability of the occupancy pattern of
rented apartments is signicant and
necessitates a frequent recalculation
of entitlement
Low Instability in occupancy: The
instability of the occupancy pattern
of rented apartments is signicant
and necessitates a frequent
recalculation of entitlement
Medium Improved occupancy stability: The
apartments with frequent turnover
rates are expected to be lower,
bringing out moderate stability in the
occupancy pattern.
Potential inequity: Being a majority,
the occupier may demand a more
equitable allocation model that
captures more nuances of the
demand variability
Flat Fee Medium Suitable for frequent turnover: Being
independent of the turnover rate of
apartments, frequent recalculation of
entitlement is prevented. May incur a
more extended payback period for
energy-efcient users
Low Potential disparity: Despite the
improved decision-making power,
the probability of disputes could be
higher due to the usage disparity
Low Inequitable for owner-occupiers: The
higher decision-making capability
might demand an equitable energy
allocation.
Electricity Demand Low Varying occupancy rate: The higher
turnover rate in rented apartments
necessitates the frequent
standardisation of energy proles,
demanding a substantial
administrative effort
Medium Recalculation of entitlement: The
share of rented apartments is still
signicant in causing frequent
recalculation of entitlement based
on energy consumption. However,
considering the support from a
higher owner-occupier population
for an equitable allocation method,
disputes could be resolved through
moderate inputs.
High Improved occupancy stability: The
apartments with frequent turnover
rates are expected to be lower,
reducing the need for frequent
standardisation
Auction Low Long payback period: The short-term
tenure of rented apartments and the
lengthy investment payback period
demotivate the tenants to bid for the
RES. Though the owners of rented
apartments can attract higher rent for
a RES-supported apartment, the
probability of a signicant share of
owners opting for RES is bleak.
Medium Fair investment from owner-
occupiers: The higher population of
owner-occupiers may be able to pool
enough bids to install the RES
Untapped solar potential: The
signicant population of renters
may result in a lack of resources to
maximise the tapping of solar
potential
High Maximise utilisation of solar
potential: Higher percentage of the
owner-occupier population may be
able to secure enough resources for
maximising the utilisation of solar
energy.
Investment Low Long payback period: The short-term
tenure of rented apartments and the
lengthy investment payback period
demotivate the tenants to bid for the
RES. Though the owners of rented
apartments can attract higher rent for
a RES-supported apartment, the
probability of all the owners opting
for RES is bleak.
Low Challenge in securing funds: The
renter population is signicant to
ensure contribution from every
apartment unit, despite being able to
sell their energy
Untapped solar potential: The
signicant population of renters
may result in a lack of resources to
maximise the tapping of solar
potential
High Adequate funds for installation of
RES: With a majority of owner-
occupiers, more decision-making
power, and nancial stability, it is
possible to invest in energy
infrastructure.
Promoting energy efcient
behaviour: An investment-based
approach can incentivize energy
efciency and renewable energy
adoption, as residents have a direct
stake in the returns on their
investments.
Lot Entitlement High Stability in allocation: Lot entitlement
is less vulnerable to modications and
is not inuenced by the turnover rate
of apartments
High Stability in allocation: Lot
entitlement is less vulnerable to
modications and is not inuenced
by the turnover rate of apartments
Medium Potential inequity: Since the owner-
occupier population is more
signicant, the owners’interests in a
more equitable allocation model may
arise as lot entitlements are based on
the property’s market value with the
least direct relation with the energy
demand.
D. Suitability analysis of building height
The height of the building inuences the energy demand across the storeys due to different exposure to external conditions. Moreover, the de-
pendency on common services such as elevators also increases with the building height, which augments the energy allocation conundrum. The
buildings are categorised as high-rise (100 to 9 storeys), mid-rise (4 to 8 storeys), and low-rise (<4 storeys) for the analysis. The following assumptions
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are incorporated to arrive at the qualitative suitability rating.
The taller the building, the higher the energy demand variation due to external conditions across the storeys. The variation is considered to be
minimal for mid-rise buildings while insignicant for low-rise buildings
Taller buildings, in general, have a higher occupancy rate
Dependency on common services such as elevators for taller buildings is signicant
Lot entitlement may not always be higher for the higher-oor apartments
The qualitative scoring of the suitability of each allocation model is pre-dened based on Table A.7. with corresponding upper and lower bounds:
high (100–71), medium (70–31), and low (30–0). The suitability scoring is calculated in three steps.
The input is a quantitative number indicating the number of storeys in the building to categorise the building as high-rise, mid-rise or low-rise.
Consequently, the qualitative scoring of each allocation model is identied from Table A.8.
Subsequently, a linear scoring method is adopted to convert the qualitative rating to a quantitative suitability rating, which varies within the pre-
dened upper and lower bounds unless the energy allocation model is independent of the typology factor.
Table A7
Suitability Ratings Based on Building Height.
Suitability Rating Denition
High Ensures equitable allocation of energy among the stakeholders
Medium The storey height may inuence the energy disparity but includes benets with minimal administrative inputs
Low There is a signicant disadvantage based on the storey level of the unit
Table A8
Suitability Scale for Building Height.
Allocation Model High-rise
(100 to
9)
Justication Mid-rise
(4 to 8)
Justication Low-
rise
(<4)
Reasoning
Equal Allocation Low Variable energy demands: Higher-oor
apartments are susceptible to varying
energy demands due to external
conditions such as exposure to sunlight
and temperature variations.
Disproportionate use of services:
Disproportionate reliance on services
such as elevators results in unfair
distribution of energy costs
Medium Moderate inuence of external
conditions: Variation in energy demand
due to external conditions is expected to
be minimal
Disproportionate use of services: The
extent of common service utilisation
may cause variations in energy demand
High Minimal dependency on common
services: Dependency on common
services and susceptibility to external
atmospheric conditions are expected
to be minimal to cause a disparity in
energy demand.
Roof Delineation Low Lower per-unit roof area: The
availability of roof space per apartment
(or energy per apartment) will reduce as
the buildings go taller, making the
installation of RES unattractive.
Increased installation cost: The
expenditure for individual installation
will also be higher, demanding more
utility services to deliver the energy to
lower-oor apartments
Challenges in arriving at a collective
consent: More the number of residents,
more the difculty in arriving at a
collective consent
Low Limited roof-space availability: Though
the overall building energy demand will
be comparatively lower, individual
delineation of roof area could pose
challenges with overshadowing, lack of
space for other installations on the roof
and limited accessibility
Challenges in arriving at a collective
consent: More the number of residents,
more the difculty in arriving at a
collective consent
Medium More per-unit roof area: Lower
number of units per roof area for
installation of individual RES
Requires careful planning: Careful
planning must be ensured to facilitate
future expansions and interests of
other stakeholders as well, which
demands moderate administrative
inputs
Floor-Area Low Variable energy demands: The energy
demand for apartments in higher storeys
is expected to be high. The apartments
with lower oor areas on higher storeys
face a disadvantage, which is augmented
by their reliance on elevators
Medium Moderate inuence of external
conditions: Variation in energy demand
due to external conditions is expected to
be minimal
Independent of common services: The
oor area of a unit has no direct
inuence on the usage of common
properties by the unit residents.
High Equitable distribution: The
dependency on common services is
minimal by the units. Hence,
allocation needs to cater to the energy
demand of the units alone, for which
oor area is a direct indicator
Occupant Count Medium Inconsistent allocation across storeys:
The singly-occupied units on higher
oors may experience a similar demand
as the doubly-occupied units on the
lower oors due to external conditions
such as increased temperature.
Accounts for CP usage: The more
occupants, the more the aggregated
usage of common services by the unit
High Equitable distribution: Energy demand
variation due to external conditions is
expected to be minimal. Moreover, this
type of allocation accounts for the usage
of CP and is a direct indicator of the
energy demand of the units
High Independent of storey height: Since the
external variations are minimal, the
energy prole is dependent on the
energy behaviour of the apartment,
which can be attributed to the
occupancy of the unit
(continued on next page)
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Energy & Buildings 320 (2024) 114601
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Table A8 (continued )
Allocation Model High-rise
(100 to
9)
Justication Mid-rise
(4 to 8)
Justication Low-
rise
(<4)
Reasoning
Flat Fee Medium Equal fee regardless of height: Flat fee
may allow the residents staying on
higher oors to compensate for the extra
demand incurred due to external
conditions, with the ability to use extra
energy for the same price as the lower
oors.
Doesn’t account for the CP usage:
Potential disparity of energy allocation
may arise across the storeys due to a
lack of motivation for reducing the
energy consumption of common services
Low Potential wastage of energy: Since
external variations are minimal, the
application of this method leads to
disputes as those who use more energy
get more benet
Low Potential wastage of energy: Since
external variations are minimal, the
application of this method leads to
disputes as those who use more energy
get more benet
Electricity Demand High Reects actual energy needs: Accounts
for the additional energy demand due to
external conditions
High Minimal inuence of storey height:
External variations due to storey height
are minimal. Hence, the apartments get
energy based on their historical energy
prole.
Opportunity to incentivise energy-
efcient users: May assist in
incentivising the energy-efcient users
since the allocation is redened based on
the historical performance of energy
usage
High Minimal inuence of storey height:
External variations due to storey
height are minimal. Hence, the
apartments get energy based on their
historical energy prole.
Opportunity to incentivise energy-
efcient users: May assist in
incentivising the energy-efcient users
since the allocation is redened based
on the historical performance of
energy usage
Auction High Equal opportunity across storeys: An
auction-based system allows residents to
bid for their desired energy allocation.
This method ensures that residents have
control over their energy usage and can
allocate their resources according to
their individual preferences
High Equal opportunity across storeys: While
the energy consumption variation may
be lower than in taller buildings, an
auction-based system still allows
residents to bid for their desired energy
allocation. This method ensures that
residents have control over their energy
usage and can allocate their resources
according to their individual preferences
High Efcient resource utilisation: Despite
being a complex process to implement,
bidding provides an opportunity for
the units that need extra energy due to
factors other than storey height and
maximises the possibility of tapping
solar energy
Investment High Assist in pooling investments: High-rise
buildings generally require heavier
investment than low-rise buildings to
adopt RES. This allocation model assists
in pooling the required investment while
ensuring the participation of all units in
investing in the required energy.
High Accounts for lifestyle choices: Though
the energy demand variations are
minimal, this method accounts for the
lifestyle of the units by enabling them to
invest in the required energy
High Accounts for lifestyle choices: Though
the energy demand variations are
minimal, this method accounts for the
lifestyle of the units by enabling them
to invest in the required energy
Lot Entitlement Low Varied allocation: Lot entitlement is
dependent on factors such as rentable
area, orientation of apartment, and
accessibility, which varies across the
storeys with a lack of consideration of
energy demand due to external
conditions
Low Varied allocation: Lot entitlement still
plays a signicant role in a medium-
sized building, with the possibility of
lower oors having a greater entitlement
based on the frontage and rentability
while keeping others on higher oors at a
disadvantage
Medium Moderate variation: The variation due
to lot entitlement is minimal. However,
there exists the possibility of minor
disputes as two similar units on the
same oor may have different lot
entitlements
APPENDIX B
The quantiable nature of nancial disparity and the cost of inaction enables the impact rating through numerical modelling. For this analysis, a
hypothetical MOB with a solar panel system on the rooftop was used as the model. The parameters such as the capital expenditure per kW, the capacity
factor of the panels, the operational expenditure, panel attributes, and the roof area covered by solar panels for energy generation are listed in
Table B.1. From these parameters, the derived inputs, such as the capacity and generation potential of solar panel systems were calculated. The model
retains these factors related to energy generation static so that the nancial disparity and cost of inaction are modelled solely based on the variation of
apartment attributes such as oor area, energy demand proles, occupancy, and lot entitlement on which Monte-Carlo simulations were performed as
per the rules laid out in Table B.2.Table B.2 also demonstrates the dynamics of the inputs for at-fee, auction, and investment-based allocations, which
may vary based on the location and industry standards and are solely used to evaluate the energy entitlement of units in a hypothetical MOB.
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Table B1
Technical Inputs for Numerical Modelling.
Features Value Units Formula/ References
Panel Output 300 W
Panel Dimensions 1.5 x 1 m
Available Roof Area considering Ground Cover 100 m2
Capacity Factor 25 % [54]
Plant Capacity 20 kW (Available Roof Area* Panel Output
1000 )
Panel Dimensions
Capital Expenditure 17,500 AU$ [55]
Operational Expenditure 17 AU$/kW [56]
Generation potential 120 kWh (Plant Capacity*Capacity Factor*24)
Table B2
Assumptions for Numerical Modelling.
Apartment
Characteristics
Particulars Assumptions
Floor Area Uniform Distribution;
Between 50 sqm and 150sqm
Based on the average recommended size for units in NSW [57];
1BHK –58 m2
2BHK –91 m2
3BHK –148 m2
Energy Demand Prole Normal Distribution;
Mean –12.5kWh;
Standard Deviation –3.75
Demand prole may vary with a higher probability of 12kWh/day
Occupancy Discrete Uniform Distribution;
Between 1 occupant and 5 occupants
Maximum 5 residents live in an apartment;
Estimated energy demand as per occupancy [58]:
1 person –10kWh/day
2 person −16kWh/day
3 person –18kWh/day
4 person –20kWh/day
5+person –25kWh/day
Lot entitlement
percentage
Floor area percentage +random number
between 1 and 100
Inclusion of randomness allows to accommodate the variation in lot entitlement due to external factors
Flat Fee Uniform distribution of energy;
Random number between 0 and 120
Random number between 0 and 120 is generated to reect the energy entitlement per day of a unit, adjusted for a
total energy entitlement of 120kWh/day
Auction Discrete Uniform Distribution;
Random number between 0 and 120
Random number between 0 and 120 is generated to reect the bid made by the unit; adjusted to have total bids at an
instant to be 120kWh
Investment Discrete Uniform Distribution;
Random number between 1 and 17,500
Random number between 1 and 17,500 is generated to reect the investment made by the units such that the total
investment is 17,500 AU$ at any instant.
The numerical model evaluates a hypothetical MOB with 16 apartment units, followed by the Monte-Carlo simulation using ‘ORACLE CRYSTAL
BALL v 11.1.3 for 10,000 iterations with the inputs varying with the rules outlined in Table B.1 and B.2., which is synonymous to considering 10,000
distinct MOBs. The nancial disparity for the allocation models is quantied by calculating the difference between the maximum and minimum
allocated energy among the units in each iteration. The difference in cost incurred with and without solar panels installed, targeting the year 2050, is
considered to determine the cost of inaction, aligning with the net-zero goals of most countries. Moreover, the average lifespan of solar panels is
putatively 25–30 years [59], making the year 2050 suitable for projecting the cost of inaction, employing the inputs in Table B.3.
While several factors, such as the potential carbon tax, federal government rebates and the inuence of electric vehicles, could inuence the cost of
inaction, this analysis primarily incorporates the effect of future electricity prices, aligning with the worst-case scenario approach. The mean results
from the 10,000 iterations are used to compare the impact of each allocation model based on nancial disparity and cost of inaction. Figure B.1.
illustrates the variation in the cost of inaction and the nancial disparity with the allocation models, transformed into an impact rating from 1 to 5,
with 1 representing the lowest impact. Table B.4. details the rubric adopted for evaluating impact ratings under the cost implications, implementation
challenges, and management challenges of the allocation models.
Although, the impact scores of the allocation models might cluster together under the assumption of equal weighting, the prioritisation of certain
factors by the stakeholders could alter the overall impact scores. Notably, the minor variations can translate into substantial variations in absolute
terms. This underscores that no single option is inherently much superior or inferior based solely on the impact scores; rather, highlighting the
importance of building typology-dependent suitability check, aligning with the practical, real-world considerations.
A. Poshnath et al.
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Table B3
Technical Inputs for ‘Cost of Inaction’Calculation.
Particulars Year 2023 Year 2050 Calculation
Capital Expenditure 17,500 AU$ 35,573.90 AU$ Future Value @ 3 % nominal ination
Operational Expenditure 340 AU$ p.a. 755.23 AU$ p.a. Future Value @ 3 % nominal ination
Electricity Price Fixed –AU$ 1.10/day
Variable –AU$ 0.22/kWh
Fixed –AU$ 5.95/day
Variable –AU$ 1.17/kWh
Future Value @ 6 % ination derived from historical data of electricity price [60]
Fig. B1. Variation of Financial Disparity and Cost of Inaction with Energy Allocation Models
Table B4
Rubric for Impact Analysis.
Impact
Rating
Cost Implications Implementation Management
1 No signicant cost of implementation/
upfront cost
Easy to implement, with minimal barriers No monitoring / Recalculation is required
2 Cost for metering/submetering or
monitoring/ Administration
Moderate barriers, such as measurement of criteria for allocation Minimal monitoring with occasional recalculation
3 Variable costing and related
uncertainties
Adequate resource planning is required as the criteria may change with
monitoring equipment
Moderate administration challenges or communications
involved with constant monitoring
4 Common cost for upgrading existing
infrastructure/ retrot
Based on agreements Demands complex operation requirements
5 Signicant cost for retrotting on
particular shoulders
Substantial risk of failure due to administrative requirements or
opposition from stakeholders, primarily when individually owned
Includes complex stakeholder dynamics, strategic
decision-making, opposition from stakeholders
APPENDIX C
The suitability of the energy allocation models can be signicantly inuenced by the typology of the building where the RES is proposed to be
installed. This section details the procedures for the performance assessment of the energy allocation models concerning the building typology. Based
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on the inputs in Table 7, the suitability score for each allocation model for typologies 1 and 2 is determined by the numerical simulation model. The
suitability scores of each energy allocation model for the typologies have been tabulated in Table C.1. and C.2.
Table C1
Variation of Suitability Scores for Typology 1.
Energy Allocation Model Age of Building CP Ownership Tenant % Building Height
Equal Allocation 2.5 0.0 65.0 29.0
Roof Delineation 47.5 0.0 15.2 27.5
Floor-Area 52.5 50.0 85.3 29.0
Occupant Count 47.5 0.0 15.2 68.7
Flat Fee 0.0 0.0 27.9 32.3
Energy Demand 100.0 50.0 36.0 100.0
Auction 100.0 50.0 36.0 100.0
Investment 100.0 50.0 15.2 100.0
Lot Entitlement 52.5 0.0 85.3 27.5
Table C2
Variation of Suitability Scores for Typology 2.
Energy Allocation Model Age CP Ownership Tenant % Building Height
Equal Allocation 35.0 100.0 12.0 70.0
Roof Delineation 15.0 50.0 54.4 30.0
Floor-Area 85.0 100.0 46.6 70.0
Occupant Count 15.0 100.0 54.4 87.6
Flat Fee 0.0 50.0 5.1 12.9
Electricity Demand 100.0 100.0 88.4 100.0
Auction 100.0 100.0 88.4 100.0
Investment 100.0 100.0 88.4 100.0
Lot Entitlement 85.0 100.0 46.6 30.0
Moreover, the energy allocation models possess inherent, uninuenced characteristics that remain unaffected by the typology factors. Utilising the
impact scores laid out in Table 6, the composite scores of the energy allocation models are determined using Equation (3) for the typology 1 and 2,
tabulated in Table C.3. and Table C.4., respectively.
Table C3
Composite Scores of Typology 1.
Allocation Model Building Age CP Ownership Tenant % Building Height
Equal Allocation 7.6 0.0 198.7 88.7
Roof Delineation 122.6 0.0 39.3 71.0
Floor-Area 149.8 142.6 243.3 82.8
Occupant Count 131.3 0.0 42.1 189.9
Flat Fee 0.0 0.0 80.2 93.0
Electricity Demand 260.7 130.3 93.8 260.7
Auction 257.6 128.8 92.7 257.6
Investment 269.0 134.5 40.9 269.0
Lot Entitlement 145.2 0.0 235.8 76.0
Table C4
Composite Scores of Typology 2.
Allocation Model Building Age CP Ownership Tenant % Building Height
Equal Allocation 107.0 305.8 36.7 214.0
Roof Delineation 38.7 129.0 140.4 77.4
Floor-Area 242.5 285.3 132.9 199.7
Occupant Count 41.5 276.4 150.3 242.0
Flat Fee 0.0 144.0 14.8 37.0
Electricity Demand 260.7 260.7 230.5 260.7
Auction 257.6 257.6 227.7 257.6
Investment 269.0 269.0 237.8 269.0
Lot Entitlement 235.0 276.5 128.8 82.9
The TOPSIS analysis validates the suitability scores derived earlier and ranks the energy allocation models based on their suitability for the specic
building typology. However, the ranking may be inuenced by the objective of the installation of the RES. Thus, performing an objective-proximity
analysis is imperative to determine the most suitable allocation model for achieving the specic objective. To facilitate this, numerical modelling is
employed to assess the suitability of the energy allocation models if the overarching goal is to attain the net-zero electricity status. The assessment
considers two distinct MOBs with ten apartment units with the technical parameters outlined in Table C.5. and Table C.6.
A. Poshnath et al.
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Table C5
Technical Parameters for Objective-Proximity Analysis of Typology 1.
Typology 1
Apartment Floor area (m2) Electricity Demand (kWh/day) No. of Occupants Lot entitlement Auctioned Energy (kWh)
Unit 1 137 20 3 110.2 21.2
Unit 2 149 15 5 99.2 12.3
Unit 3 106 18 2 96.4 4.8
Unit 4 90 9 5 74.0 21.6
Unit 5 136 17 1 75.1 42.1
Unit 6 67 17 2 43.0 3.7
Unit 7 106 13 5 12.4 4.1
Unit 8 66 19 5 84.9 5.2
Unit 9 126 10 5 55.2 1.5
Unit 10 142 20 2 63.6 3.4
Table C6
Technical Parameters for Objective-Proximity Analysis of Typology 2.
Typology 2
Apartment Floor area (m2) Electricity Demand (kWh/day) No. of Occupants Lot entitlement Auctioned Energy (kWh) Invested Energy (kWh)
Unit 1 53 7 2 93.5 14.0 16.9
Unit 2 90 6 1 75.3 8.9 13.0
Unit 3 55 8 2 14.7 4.9 12.2
Unit 4 93 14 2 106.6 12.2 9.8
Unit 5 119 11 3 106.2 0.9 10.0
Unit 6 146 11 2 88.0 13.7 12.8
Unit 7 110 15 2 39.3 13.1 17.8
Unit 8 64 8 3 45.6 21.1 4.1
Unit 9 115 7 4 43.8 24.4 20.2
Unit 10 127 8 5 14.1 6.7 3.3
The relevant inputs from Table B.2. are used to evaluate the allocated energy of each apartment unit for the building typologies. Particularly, the
three models deemed highly suitable are considered for the performance evaluation of the attainment of net-zero electricity status. The analysis
evaluates each apartment’s electricity imported from the grid based on the difference between the respective electricity demand and the allocated
energy. Tables C.7. and C.8 indicate the per-apartment electricity import and the aggregated electricity import data for both typologies. The values in
the parentheses next to each gure indicate the excess energy available for the apartment units, signifying net-positive energy apartments. The energy
allocation models are compared based on the net electricity import from the grid, and rankings are juxtaposed with those from the TOPSIS analysis.
Table 10 clearly highlights the necessity of performing the objective proximity analysis as the rankings differ from the TOPSIS ranking. This
nonconformity spotlights that while TOPSIS offers a preliminary assessment of model suitability, the objective proximity analysis is essential for the
accurate choice of allocation models to meet the specic goals of the installation of RES.
Table C7
Evaluation of electricity import from the grid for typology 1.
Typology 1
Electricity Demand Auction Floor Area
Apartment Entitled Energy
(kWh/day)
Imported Electricity
(kWh/day)
Entitled Energy
(kWh/day)
Imported Electricity
(kWh/day)
Entitled Energy
(kWh/day)
Imported Electricity
(kWh/day)
Unit 1 15.2 4.8 21.2 (1.2) 14.6 5.4
Unit 2 11.4 3.6 12.3 2.7 15.9 (0.9)
Unit 3 13.7 4.3 4.8 13.2 11.3 6.7
Unit 4 6.8 2.2 21.6 (12.6) 9.6 (0.6)
Unit 5 12.9 4.1 42.1 (25.1) 14.5 2.5
Unit 6 12.9 4.1 3.7 13.3 7.1 9.9
Unit 7 9.9 3.1 4.1 8.9 11.3 1.7
Unit 8 14.4 4.6 5.2 13.8 7.0 12.0
Unit 9 7.6 2.4 1.5 8.5 13.4 (3.4)
Unit 10 15.2 4.8 3.4 16.6 15.1 4.9
Total Imported
Electricity
38.0 77.0 43.1
A. Poshnath et al.
Energy & Buildings 320 (2024) 114601
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Table C8
Evaluation of electricity import from the grid for typology 2.
Typology 2
Investment Electricity Demand Auction
Apartment Entitled Energy
(kWh/day)
Imported Electricity
(kWh/day)
Entitled Energy
(kWh/day)
Imported Electricity
(kWh/day)
Entitled Energy
(kWh/day)
Imported Electricity
(kWh/day)
Unit 1 16.9 (9.9) 8.8 (1.8) 14.0 (7.0)
Unit 2 13.0 (7.0) 7.6 (1.6) 8.9 (2.9)
Unit 3 12.2 (4.2) 10.1 (2.1) 4.9 3.1
Unit 4 9.8 4.2 17.7 (3.7) 12.2 1.8
Unit 5 10.0 1.0 13.9 (2.9) 0.9 10.1
Unit 6 12.8 (1.8) 13.9 (2.9) 13.7 (2.7)
Unit 7 17.8 (2.8) 18.9 (3.9) 13.1 1.9
Unit 8 4.1 3.9 10.1 (2.1) 21.1 (13.1)
Unit 9 20.2 (13.2) 8.8 (1.8) 24.4 (17.4)
Unit 10 3.3 4.7 10.1 (2.1) 6.7 1.3
Total Imported
Electricity
13.8 0.0 18.2
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Glossary
CP: Common Property
MCDA: Multi-Criteria Decision Analysis
MOB: Multi-Owned Building
OC: Owners’Corporation
PV: Photovoltaic
RES: Renewable Energy System
SDG: Sustainable Development Goals
TOPSIS: Technique for Order of Preference by Similarity to Ideal Solution
A. Poshnath et al.
