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Storage and Demand Side Management (DSM) are key in integrating renewable energy into community energy systems. There are many modelling tools which support design of such systems. In order to select an appropriate tool it is essential to understand tool capabilities and assess how these match requirements for a specific situation. The aim of this paper is to provide a process to be used to make such a selection consisting of: (i) a tool capability categorisation, (ii) a stepwise tool selection process. Capabilities of 13 tools (screened from 51) for community scale were categorised covering: input data characteristics; supply technologies; design optimisation; available outputs; controls and DSM; storage; and practical considerations. A stepwise selection process is defined, adapted from software engineering, in which tools are scored based on ‘essential’, ‘desirable’, or ‘not applicable’ technical capabilities for the specific situation. Tools without essential capabilities are eliminated. Technical scores and practical considerations are then used to select the tool. The process is demonstrated for a simple case study. The future applicability of the selection process is discussed. Findings from the capability categorisation process are highlighted including gaps to be addressed and future trends in modelling of such systems.
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Sustainable Cities and Society
journal homepage: www.elsevier.com/locate/scs
A modelling tool selection process for planning of community scale energy
systems including storage and demand side management
Andrew Lyden
,1
, Russell Pepper
,1
, Paul G. Tuohy
Energy Systems Research Unit (ESRU), University of Strathclyde, Glasgow G1 1XJ, UK
ARTICLE INFO
Keywords:
Modelling
Demand side management
Battery storage
Thermal storage
Renewable energy
Community energy systems
District energy systems
Distributed generation
Design tools
Selection process
ABSTRACT
Storage and Demand Side Management (DSM) are key in integrating renewable energy into community energy
systems. There are many modelling tools which support design of such systems. In order to select an appropriate
tool it is essential to understand tool capabilities and assess how these match requirements for a specic si-
tuation. The aim of this paper is to provide a process to be used to make such a selection consisting of: (i) a tool
capability categorisation, (ii) a stepwise tool selection process.
Capabilities of 13 tools (screened from 51) for community scale were categorised covering: input data
characteristics; supply technologies; design optimisation; available outputs; controls and DSM; storage; and
practical considerations.
A stepwise selection process is dened, adapted from software engineering, in which tools are scored based on
essential,desirable,ornot applicabletechnical capabilities for the specic situation. Tools without essential
capabilities are eliminated. Technical scores and practical considerations are then used to select the tool. The
process is demonstrated for a simple case study.
The future applicability of the selection process is discussed. Findings from the capability categorisation
process are highlighted including gaps to be addressed and future trends in modelling of such systems.
1. Introduction
1.1. Community scale energy systems
Energy systems worldwide are undergoing a transition towards
sustainability driven by three primary goals: energy security, energy
equity, and environmental sustainability (World Energy Council, 2015).
One impact is increasing use of renewable energy through community
scale energy systems. These systems have been the subject of a range of
research including technical analysis (Ahadi, Kang, & Lee, 2016;
Bhattacharyya, 2012;Chmiel & Bhattacharyya, 2015;Deshmukh &
Deshmukh, 2008), socio-economic studies (Rogers, Simmons, Convery,
& Weatherall, 2008;Walker, Devine-Wright, Hunter, High, & Evans,
2010), and environmental and institutional studies (Koirala, Koliou,
Friege, Hakvoort, & Herder, 2016;Rae & Bradley, 2012) which identify
important roles for such systems in the future.
Community scale energy systems are being promoted by policy.
They accounted for 22% of installed renewable electricity capacity in
2012 in Germany (Romero-Rubio & de Andrés Díaz, 2015), UK policy is
for these systems to provide 8% of renewable electricity capacity by
2020 (Capener, 2014), and in Scotland there is a target for 2GW by
2030 (Scottish Government, 2017). In Denmark, local communities
attract preferential shares in local wind projects (Danish Government,
2008).
One challenge concerning the use of renewable resources is that
they are often stochastic, causing supply to demand mismatch. Storage
and DSM can address this by decoupling the dynamics of supply and
demand. Storage and DSM can enable supply to demand matching at
various timescales e.g. systems that react in the order of seconds to
balance grid voltage or frequency deviations, or systems that allow load
shaping on half-hourly or hourly scales over day or part day horizons to
accommodate renewables or achieve lowest cost (Ganu et al., 2012).
Future community systems may also include longer timeframe seasonal
storage potentially through energy vectors such as hydrogen, or fuel
synthesis such as production of green methane or methanol etc. It is
proposed that the integration of electricity, thermal and transport sys-
tems should be considered to achieve an overall optimum (Mathiesen
et al., 2015).
In this paper DSMis used to describe mechanisms for adjusting
loads on the demand side i.e. downstream of the generation point(s).
https://doi.org/10.1016/j.scs.2018.02.003
Received 14 August 2017; Received in revised form 25 January 2018; Accepted 6 February 2018
Corresponding authors.
1
Both contributed equally to this paper.
E-mail addresses: andrew.lyden@strath.ac.uk (A. Lyden), russell.pepper@strath.ac.uk (R. Pepper).
Sustainable Cities and Society 39 (2018) 674–688
Available online 09 February 2018
2210-6707/ © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/BY/4.0/).
T
Storageis used to mean both explicit thermal, electrical and chemical
storage systems and also the inherent storage capacity available in
buildings and systems due to their thermal inertia. Storage may be
utilised through appropriate controls to provide DSM services.
Explicit storage technologies can be in many forms e.g. batteries
(Crespo Del Granado, Wallace, & Pang, 2014), night time ceramic
thermal storage (Strbac, 2008), water or phase change material based
thermal energy stores (Arteconi, Hewitt, & Polonara, 2013), and che-
mical storage through energy vectors such as hydrogen (Agbossou et al.,
2001). Energy systems can also utilise inherent storage e.g. in buildings,
distribution networks, fridges and freezers etc. Other DSM techniques
include encouraging behavioural change in consumers (Campillo,
Dahlquist, Wallin, & Vassileva, 2016).
1.2. Modelling tool selection for community scale energy systems
Given the importance of community scale energy systems, wide
variation in possible supply, storage control options, and dierent
contexts such as climates and user expectations, there have been many
eorts to provide modelling support for the planning process from a
range of dierent perspectives. A general method for community en-
ergy planning is described in (Huang, Yu, Peng, & Zhao, 2015); a key
element identied is the use of modelling tools. Many tools have been
developed and applied to a range of situations.
EnergyPLAN (University of Aalborg, 2017a) is a national and re-
gional planning tool which has been used to model a 100% renewable
energy future for Denmark (Lund & Mathiesen, 2009) and for many
other studies (University of Aalborg, 2017b). It is applicable at com-
munity scale, and was used to model the island of Mljet in Croatia
(Lund, Duić, Krajacić, & Graça Carvalho, 2007) in a comparative study
with H
2
RES, an alternative tool designed for simulating the integration
of renewables and hydrogen storage into island systems (University of
Zagreb, 2009). In this study, it was shown that both tools gave very
similar results; H
2
RES focus is technical while EnergyPLAN supports
technical and economic analyses. Both tools are deterministic and used
an hourly energy balance over a year to calculate energy generated,
stored, rejected, consumed, exported, lost, and produced in excess, as
well as percentage of energy consumed from renewable sources.
HOMER (HOMER Energy, 2017c) is a community-scale tool, origin-
ally developed to support design of o-grid community scale electrical
energy systems but expanded to model grid connected and thermal
systems (HOMER Energy, 2017b). One example is modelling a hybrid
solar-biomass system for a remote area in Pakistan (Shahzad et al.,
2017). This study used electricity demand, available solar and biomass
resource, and costs to analyse the techno-economic viability of such a
system. HOMER was used to optimise system size using an hourly energy
balance and with minimum net present cost (NPC) as objective function.
Merit (University of Strathclyde, 2015) is another community-scale
tool which has been used to model a hybrid wind/solar system for a
care home in Scotland (Morton, Grant, & Kim, 2017). Merit models
demands, supply and storage using an hourly energy balance and pro-
vides results showing demand/supply match and renewable and non-
renewable supply. Multiple systems were modelled, and those shown to
satisfy demand all year round analysed. The tool provides technical
analysis only with cost calculations being done outside of the tool.
TRNSYS (TRNSYS, 2017) has a user-dened time step as small as
1 s. A comprehensive library of components is available. Systems are
described in detail and the solver is dynamic which means that TRNSYS
is usually a building-level simulation tool (Beausoleil-Morrison et al.,
2012); the number of components and parameters required for a
community scale system could be complex requiring expert level of
technical systems knowledge and complex calculations take consider-
able time. It has been used to model hybrid solar PV/thermal systems
with thermal and electrical storage (Kalogirou, 2001) etc. TRNSYS and
similar building level simulation tools can be scaled up for use at
community-scale.
The tools described above are a sample of those available and serve
to illustrate dierent approaches. There is general agreement that
hourly modelling timesteps (or less) are required to adequately model
such systems (Lambert, Gilman, & Lilienthal, 2006). Tools are often rst
developed from a specic perspective e.g. hydrogen for H2RES, o-grid
for HOMER, building systems for TRNSYS, and then adapted to support
broader planning of community scale systems. How to choose between
the plethora of dierent tools, particularly for planning of renewable
energy systems where storage and DSM are to be considered, is a key
challenge to be addressed in this paper.
A number of reviewers have previously provided an overview of
modelling tool capabilities specic to the eective integration of re-
newable energy. In general it was found that the prior work, although
extremely useful foundation for the work of this paper, did not: (i)
address all storage and DSM options, (ii) provide a suciently detailed
categorisation of the models used to represent storage and DSM, (iii)
provide a structured tool selection process. The most relevant of these
previous works are briey described below.
(Connolly, Lund, Mathiesen, & Leahy, 2010) reviewed 37 tools (nar-
rowed down from 68) regarding their suitability for the integration of
renewable energy into energy systems; the details on the storage tech-
nologies used in the tools are high level i.e. stating whether a tool is
capable of modelling pumped hydroelectric, battery, compressed air and
hydrogen storage. Thermal storage and DSM are not included in the
provided tables; thermal storageis mentioned for three of the tools in
textual descriptions. The underlying models for electrical and thermal
storages are not discussed in detail; such information can be useful to
inform tool selection as some models can be more accurate than others
(Copetti, Lorenzo, & Chenlo, 1993;Dumont et al., 2016). The authors
provide the review to inform tool selection and the provided information is
indeed useful in this regard but a formal selection process is not specied.
(van Beuzekom, Gibescu, & Slootweg, 2015) considered 72 tools to
nd those capable at city scale of modelling multi energy systems
considering all relevant energy carriers (electricity, heating, cooling,
transport etc.). They considered in detail 13 of the tools which were
open source. Information regarding the tools was usefully tabulated
including: available RES components, storage options, economic para-
meters, scale, availability, objective, modelling approach, time step,
evaluation criteria, user friendliness and training requirement. The
paper identied the dierent storage technologies included in the en-
ergy tools but did not give detail on the underlying models. While it was
highlighted that grid balancing is essential in districts utilising sto-
chastic energy sources, the DSM and grid support modelling capability
of the tools was not captured. No tool selection process was specied.
(Allegrini et al., 2015) reviewed 20 tools chosen based on their
ability to simulate and analyse urban energy systems. Storage dis-
cussion was limited to seasonal thermal storage modelling, with
building level storage capability documented within the tables but not
in detail, DSM also is not covered in detail.
Several further reviews of energy system tools have been under-
taken. (Keirstead, Jennings, & Sivakumar, 2012) reviewed 219 studies,
examining areas of urban energy systems (technology design, building
design, urban climate, systems design, policy assessment, land use and
transportation modelling) to evaluate their potential for integrated
urban design. (Mendes, Ioakimidis, & Ferrao, 2011) reviewed 6 bottom-
up tools which focus on optimisation of community energy systems,
nding DER-CAM and MARKAL/TIMES to be the most appropriate.
(Markovic, Cvetkovic, & Masic, 2011) documented the capabilities and
inputs/outputs of 11 energy tools, a short paragraph on each was
provided in terms of their energy, economic and environmental analysis
capabilities. (Mirakyan & De Guio, 2013) undertook a review of 12
tools to consider the methods available for integrated energy analysis
for cities and territories. These reviews all lack details on storage and
DSM functionality and modelling, and none provide any tool selection
process.
In other elds of engineering, standard methodologies such as the
A. Lyden et al. Sustainable Cities and Society 39 (2018) 674–688
675
software tool selection processby Sandia National Laboratories (Lin
et al., 2007) systematically evaluate, rank and select software tools
according to user requirements. The Sandia process has the following
steps: (i) gather requirements and sort these into essentialand de-
sirablecategories, (ii) assess quality of software manufacturer, soft-
ware quality and compatibility, and (iii) assess the software against
requirements and select the best t. The process focuses rst on nding
tools with all essentialcriteria, which are then ranked based on de-
sirablecriteria. An extension of the methodology was proposed (Jong,
Hernandez, Post, & Taylor, 2011) which uses pair-wise comparison to
aid in weighting of desirablerequirements in addition to a Pugh Table
to aid tool selection. It would appear that this approach could be use-
fully adapted to support a tool selection process for community scale
energy systems including storage and DSM.
1.3. Aims, methodology and scope
The specic aims of this paper are: (i) to categorise and document
capabilities of tools suitable for modelling community systems for the
planning design stage with focus on incorporation of storage and DSM,
and (ii) develop a selection process based on these documented cap-
abilities to identify tools suitable for modelling in a specic situation.
This will be achieved through:
An initial screening process to identify potentially suitable tools.
Categorisation and tabulation of modelling tool capabilities and
characteristics.
Development of a tool selection process using the tables.
Demonstration of the selection process for a case study.
Discussion of the ndings.
The scope of the work presented here has been limited to tools
designed for hourly or sub hourly timestep modelling of community
systems containing low-carbon technology, storage and DSM, for use at
the planning stage. More detailed building and system design tools have
been considered outside of the scope of this paper.
There is an increasing trend towards using modelling tools in con-
junction with other modelling tools or external software such as
MATLAB (Bava & Furbo, 2017), GEN-OPT (Wetter, 2000), En-
ergyTRADE (EMD, 2017a) etc. particularly to support mathematical
optimisations or realistic controls. These multi-tool processes are also
outside the main focus of this paper but will be discussed at the end.
The authors recognise that tools are continuously being developed
and that the screening analysis and the tool classication exercise will
need to be refreshed periodically. The work of this paper, in addition to
providing a current snapshot, provides a useful framework for this re-
fresh within the context of the proposed tool selection process.
2. Initial screening process to identify potentially suitable tools
An initial list of 51 tools with some ability to model an energy
system was derived from: literature including review papers and papers
describing the development and application of tools; tool user manuals
and websites; and communications with tool providers. Tools captured
in previous reviews but clearly not capable of modelling community
scale energy systems were discounted, for example, Envi-met is a mi-
croclimate and landscaping tool (ENVI_MET, 2017), and Radiance is
used in daylight prediction (Lawrence Berkeley National Lab, 2017).
A set of criteria were applied to the 51 tools in order to determine in
more detail their potential suitability (Table 1). A tool passed the cri-
teria if it could be used at community scale (i.e. was dened as such or
had a case study demonstrating this capability), was appropriate to the
planning stage, incorporated renewable and low carbon technology and
storage and DSM, had hourly or sub-hourly timestep and could cover
either thermal or electrical energy supply. The screening process is
captured in Table 1 along with relevant references.
This process resulted in the identication of 15 tools suitable for
modelling community scale energy systems incorporating renewable
energy sources, storage and DSM, for use at planning design stages. Two
of the 15, MODEST and Mesup/PlaNET were discounted due to lack of
accessible information required for more detailed analysis. This left 13
tools to be carried forward into the categorisation of capabilities and
tool selection process.
Further details of the initial screening criteria are given below.
Community scale: This criterion is met if the tool manual, guidance
documentation or associated publications had specically described the
tool as applicable at community scale.
Community scale case study: Some tools identied as being pri-
marily for nationalor regionalplanning rather than for community
scale had available case studies or other documentation demonstrating
application at community scale so were included, study references are
given in the table.
Planning-level design: Tools capable of modelling for planning-level
design were deemed to be in scope (see Section 1.3) and to pass this
criterion. More detailed building or system design tools, which require
very detailed user inputs to describe each individual building and
system component, were deemed not to meet the criteria.
Low or zero carbon technologies (LZCT): Modelling of at least one
low-carbon or renewable technology was imposed as a minimum.
Storage and DSM functionality: Modelling of at least one form of
storage and DSM was imposed as a minimum.
Time step: Criterion met if capable of a time step of one hour or less.
Electrical and/or thermal modelling: The criterion imposed was the
ability to either model electrical or thermal networks. Community
systems can consist of electrical, thermal and transport demands;
electrical and thermal generating components; microgrid networks;
transport fuel systems; thermal networks; and various DSM
Table 1
Initial tool screening (Connolly et al., 2010;The Balmorel Open Source Project, 2017;U.S Department of Energy, 2016;The Carbon Trust, 2017;Allegrini et al., 2015;Robinson et al.,
2009;Ruan, Cao, Feng, & Li, 2017;Connolly et al., 2010;van Beuzekom et al., 2015;DECC, 2017;Marnay et al., 2013;Mendes et al., 2011;Connolly et al., 2010;Connolly et al., 2010;
Vogstad, 2000;Lund et al., 2007;University of Aalborg, 2017a;EMD, 2017b;Kiss, 2015;Connolly et al., 2010;Mirakyan & De Guio, 2013;van Beuzekom et al., 2015;Beausoleil-
Morrison et al., 2012;University of Strathclyde, 2017;Drouet & Thénié, 2009;Mirakyan & De Guio, 2013;ORDECSYS, 2017;Bakken & Skjelbred, 2007;Bakken, Skjelbred, & Wolfgang,
2007;Connolly et al., 2010;U.S Department of Energy Oce of Science & Argonne National Laboratory, 2017;Duić& da Graca Carvalho, 2004;Lund et al., 2007;Neves, Silva, &
Connors, 2014;Chmiel & Bhattacharyya, 2015;HOMER Energy, 2017c;Sinha & Chandel, 2015;Baring-gould, 1996;Mills & Al-Hallaj, 2004;Connolly et al., 2010;Ulleberg & Moerkved,
2008;Allegrini et al., 2015;Phrakonkham, Le Chenadec, Diallo, & Marchand, 2009;Sinha & Chandel, 2014;University of Zaragoza, 2017;Connolly et al., 2010;Mirakyan & De Guio,
2013;Connolly et al., 2010;Connolly et al., 2010;Ragwitz et al., 2005;Baetens et al., 2012;Baetens et al., 2015;Connolly et al., 2010;Mirakyan & De Guio, 2013;Swan & Ugursal, 2009;
Wirth et al., 2015;Comodi, Cioccolanti, & Gargiulo, 2012;Faraji-Zonooz, Nopiah, Yusof, & Sopian, 2009;Born, 2001;Connolly et al., 2010;Mirakyan & De Guio, 2013;Prasad, Bansal, &
Raturi, 2014;Bakken & Skjelbred, 2007;Cai, Huang, Lin, Nie, & Tan, 2009;Connolly et al., 2010;Mirakyan & De Guio, 2013;Connolly et al., 2010;Connolly et al., 2010;Henning, 1997,
1998;Connolly et al., 2010;NEPLAN, 2017;Carpaneto, Lazzeroni, & Repetto, 2015;Olsthoorn, Haghighat, & Mirzaei, 2016;Hadley & Hirst, 2008;Prasad et al., 2014;Connolly et al.,
2010;Bava & Furbo, 2017;Vela Solaris, 2017;Blok, Jager, & Hendriks, 2001;Olsthoorn et al., 2016;Connolly et al., 2010;SINTEF, 2017;Connolly et al., 2010;Lambert et al., 2006;Choi
& Yun, 2015;Connolly et al., 2010;Mancarella, 2014;Mirakyan & De Guio, 2013;Connolly et al., 2010;Herbergs, Lehmann, & Peter, 2017;Wirth et al., 2015;Technical University of
Denmark, 2017;Ancona, Bianchi, Branchini, & Melino, 2014;Schneider Electric Software LLC, 2017;Allegrini et al., 2015;Connolly et al., 2010;Kalogirou, 2001;Sinha & Chandel, 2014;
TRNSYS, 2017;van Beuzekom et al., 2015;Connolly et al., 2010;Connolly et al., 2010;Connolly et al., 2010).
(continued on next page)
A. Lyden et al. Sustainable Cities and Society 39 (2018) 674–688
676
Table 1 (continued)
Dark shading indicates failure of the criteria.
Light shading indicates potential failure of the criteria.
A. Lyden et al. Sustainable Cities and Society 39 (2018) 674–688
677
technologies interacting across the spectrum. Integration of these en-
ergy sectors can provide synergistic benets, often resulting in a higher
penetration of renewable supply (Mancarella, 2014;van Beuzekom
et al., 2015). While an ideal energy system tool would combine all these
energy vectors, it was recognised that many community system design
tasks utilise just one, so this was set as the minimum criteria.
3. Categorisation of modelling tool capabilities
Tool capabilities tables were generated for the 13 modelling tools
that document:
1. Input data requirements and input support capabilities.
2. Electrical and thermal supply technology modelling capabilities in-
cluding district heating.
3. Design optimisation, outputs capabilities, controls and DSM mod-
elling capabilities.
4. Storage modelling capabilities and underlying storage models.
5. Practical considerations
These tables are intended to be useful in the tool selection process
(described later in Section 4) by providing information on the capability
of tools to be assessed against requirements for a specic community
system analysis.
3.1. Input data requirements and input support capabilities
Tools have dierent levels of input data requirements; some tools
require the energy demand proles, local climate, system character-
istics, or generation proles to be explicitly input as time series directly
by the user. Other tools have embedded functions and libraries that
provide support in generating detailed datasets from simple inputs,
and/or support a mix of both directly entered and tool generated cal-
culation inputs. This functionality could be essential, desirable, or not
applicable depending on availability of data or expertise.
The key characteristics related to data input requirements for the
various tools are captured in and described below.
3.1.1. Demand prole generator
Tools were deemed to contain a demand prole generator (Yesin
Table 2) if functionality exists to support synthesis of electrical, thermal
or fuel demand proles in hourly or sub-hourly time steps from simple
inputs such as monthly or annual bill data or descriptions of building
numbers and types, demographics, etc. Others which take the approach
that either explicit half hourly or hourly metered data needs to be ob-
tained, or potentially generated using a secondary modelling process
(e.g. using building performance simulation tools), were categorised as
Nofor this category.
3.1.2. Resource assessor
A resource assessor gives access to weather and other resources (e.g.
solar radiation, wind, water, biogas and biomass) in a suitable data
input format (e.g. from national or international datasets) based on
simple inputs (e.g. location). The resources covered were identied for
each tool.
3.1.3. Supply prole generator
A supply prole generator provides electric, thermal or fuel-pro-
ducing system outputs for use in the modelling. Modellerdescribes a
tool which generates the supply prole from the resource input (e.g.
climate) and the device specications. For example, in HOMER, local
wind speeds (the resource input) and a specic wind turbine speci-
cation (a power curve and other details) are used to calculate the wind
turbine supply prole. Database and inputdescribes a tool where the
hourly or sub hourly supply proles are input directly requiring the
user to do some outside tool calculations or source such datasets.
3.2. Electrical and thermal supply technology modelling capabilities
Tools vary with respect to the range of supply technologies that can
be directly modelled. Table 3. captures information about available
supply technologies within the dierent tools and more detailed de-
scription is given below.
A wide range of electrical supply systems can be modelled, most tools
support modelling of connection to the external electricity grid. Two
categories have been assigned for modelling of the grid connection: Grid
simpleallows for limitless import and export, with static pricing; more
complex Gridmodels include features such as connection limits and
charges, complex time based import and export tarisetc.
The modelling of district heating systems, if available in the tools, is
only as an estimated heat loss. This is a continuous heat loss as a per-
centage of peak load in the Biomass decision support tool, or a per-
centage of real-time load as in EnergyPRO. The heat demand density,
distribution temperature and other factors such as controls which have
a large eect on ancillary energy use and losses in district systems are
not directly considered and are required to be captured by the user in
inputting thermal demand proles.
District heating is becoming more popular in the UK (Burohappold
Engineering, 2016;Energy and Utilities Alliance, 2016), and is ubi-
quitous in Scandinavia and Eastern and Central Europe (Euroheat,
2015). It has potential to increase energy system overall eciency and
provide exibility for more eective use of waste heat and renewables
using thermal storage which is much cheaper at district scale than for
individual buildings and much cheaper than an equivalent capacity of
electrical storage (Lund et al., 2016). It is therefore important to con-
sider district heating while it will not necessarily be appropriate in all
circumstances.
3.3. Design optimisation and output capabilities
Two attributes important in supporting design tasks are: the cap-
ability of the tool to aid the identication of optimum design solutions,
and the ability of the tool to directly provide outputs required to sup-
port decision making. Key capabilities of the 13 tools in these areas are
captured in the rst two columns of Table 4 and further discussed
below.
Table 2
Input data support capabilities.
Tools Demand prole
generator
Resource
assessor
Supply prole
generator
Biomass decision
support tool
Yes No Modeller
COMPOSE No No Database and
input
DER-CAM No S, T, Wi Modeller
EnergyPLAN No No Database and
input
EnergyPRO Yes B, H, S, T, Wi Modeller
eTransport Yes Yes
a
Modeller
H2RES No B, H, S, Wi Modeller
HOMER Yes B, H, S, T, Wi Modeller
Hybrid2 Yes S, Wi Modeller
iHOGA Yes H, S, Wi Modeller
MARKAL/TIMES No B, H, S, T, Wi Modeller
Merit Yes S, T, Wi Modeller
SimREN Yes Yes
a
Modeller
Resource Assessor Key: Biomass (B); Hydro (H); Solar radiation (S); Temperature (T);
Wind (Wi).
a
indicates that a resource assessor exists but the specics were unable to be de-
termined.
A. Lyden et al. Sustainable Cities and Society 39 (2018) 674–688
678
3.3.1. Design optimisation
Optimisation tools nd the minima, or maxima, for a dened ob-
jective function by systematically searching a dened modelling space
according to a mathematical algorithm. Design optimisation involves a
search for the optimal system w.r.t. combination and sizing of compo-
nents. Most of the reviewed tools where they support optimisation use a
full factorial deterministic approach based on user dened inputs to
solve the optimisation problem and use a simple nancial and/or
carbon emissions objective. HOMER historically has executed a grid
search based on user dened inputs specifying the system options to be
included but recently provided an update allowing users to only input
upper and lower limits to the grid search. iHOGA was the only identi-
ed tool with multi-objective function capability, it includes a choice of
available objective functions and embedded genetic algorithms (Dufo-
Lopez, Cristobal-Monreal, & Yusta, 2016). The Biomass decision sup-
port tool supports the optimisation of thermal storage size. A number of
reviews have covered the mathematical optimisation methods that
could potentially be employed (Baños et al., 2011;Iqbal, Azam, Naeem,
Khwaja, & Anpalagan, 2014). Tools which do not directly support
mathematical optimisation could be used within an external mathe-
matical optimisation process by an iterative approach, but this can be
logistically complex or require advanced software skills to automate.
3.3.2. Outputs
The outputs are key in assessing system performance. Dierent tools
focus on dierent aspects of the system performance; most tools provide
nancial analysis such as cost/kWh of energy produced or information
on energy market interactions, some are purely technical and focus on
the energy production, system analysis, demand/supply match, or fuel
consumption, others assess emission and renewable penetration, and
others consider social factors such as job creation and the human de-
velopment index. Specic tool outputs can be used in external calcu-
lations to generate a wider range of analysis outputs but only the in-tool
capabilities are documented here.
3.4. Control modelling capabilities including DSM
The ability to correctly capture controls is important in assessing the
performance of community scale energy systems and particularly so
when assessing the impacts of storage and DSM in such systems.
Modelling tools often have in-built control logic intended to mimic real
or idealised controls, it is important to comprehend and assess the
Table 3
Electrical and thermal supply technologies and district heating.
Tools Electrical supply Thermal supply District heating
Biomass decision support tool No FBo Yes
COMPOSE B, C, CHP, G, Gr, PV, Wi CHP, EBo, FBo, HP, ST No
DER-CAM CHP, D, G, Gr, PV, Wi CHP, EBo, FBo, Geo, HP, ST No
EnergyPLAN B, C, CHP, D, G, Geo, Gr, GrS, H, N, PP, PV, T, Wa, Wi CHP, EBo, FBo, Geo, HP, I, ST, Was Yes
EnergyPRO B, C, CHP, D, G, Gr, H, PV, Wi CHP, EBo, FBo, HP, ST Yes
eTransport CHP, Gr, PP CHP, FBo, HP Yes
H2RES B, C, D, G, GrS, H, PV, Wa, Wi, EBo, FBo No
HOMER B, C, CHP, D, G, Gr, H, PV, Wi CHP, FBo No
Hybrid2 D, PV, Wi None No
iHOGA D, G, Gr, H, PV, Wi None No
MARKAL/TIMES B, C, CHP, D, G, Geo, Gr, GrS, H, N, PP, PV, T, Wa, Wi CHP, EBo, FBo, Geo, HP, I, ST, Was No
Merit C, CHP, G, GrS, PV, Wi, CHP, HP, ST No
SimREN Geo, H, PP PV, Wi CHP No
Key:
Electrical: Biomass power plant (B); Coal power plant (C); Combined heat and power plant (CHP); Diesel plant (D); Gas plant (G); Geothermal plant (Geo); Grid (Gr); Grid simple (GrS);
Hydro (H); Nuclear (N); Generic power plant (PP), Photovoltaic (PV); Tidal (T); Wave (Wa); Wind (Wi).
Thermal: Combined heat and power (CHP); Electric boiler (EBo); Fuel boiler (FBo); Geothermal (Geo); Heat pump (HP); Industrial surplus (I); Solar thermal (ST); Waste incineration
(Was).
Table 4
Design optimisation, outputs, controls and DSM controls capabilities.
Tools Design optimisation Outputs Controls DSM control
Biomass decision support tool S E, EP, FA, FC, RP, SA FO, NO FO
COMPOSE E, F E, EP, FA, FC, SA MO, OO (F) OO (F)
DER-CAM E, F A, E, EP, FA, FC, SA DC, EV, LS, MO, OO (F, E) DC, EV, LS, OO (F, E)
EnergyPLAN No E, EP, FA, FC, SA, RP FO, LS, MO, OO (F) FO, LS, OO (F)
EnergyPRO No E, EMI, EP, FA, FC, SA EV, MO, NO, OO (F), UO EV, OO (F)
eTransport F E, EMI, EP, FA, FC, SA MO, OO (F) OO (F)
H2RES No EP, FC, RP, SA FO, MO FO
HOMER F A, E, EP, FA, FC, RP, SA, AC, LS, MO, NO, OO (F),
UO
LS, OO (F)
Hybrid2 No EP, FA, SA FO, LS, MO, NO FO, LS
iHOGA Single: F Double or triple: combination of A, E, F, HDI,
JC, NPC
A, E, EP, FA, FC, HDI, JC, RP,
SA
FO, MO, NO, OO (F) FO, OO (F)
MARKAL/TIMES F E, EMI, EP, FA, FC, RP, SA, MO, NO, OO (F) OO (F)
Merit No EP, FC, M, SA FO, LS, MO FO, LS
SimREN No EMI, EP, SA ––
Key:
Design Optimisation: Autonomy (A); Emissions (E); Financial (F); Human development index (HDI); Job creation (JC); System (S).
Outputs: Autonomy (A); Emissions (E); Energy market interaction (EMI); Energy production (EP); Financial analysis (FA); Fuel consumption (FC); Human development index (HDI); Job
creation (JC); Demands/supply match (M); Renewable penetration (RP); System analysis (SA).
Controls/DSM Controls: Advanced control (AC); Demand curtailment (DC); Electric vehicles (EV); Fixed order (FO); Load shifting (LS); Modulating output (MO); Non-modulating output
(NO); Operational optimisation (OO) with objective function in brackets; User-dened order (UO).
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control regime underpinning each of the models. Key capabilities of the
13 tools are captured in Table 4 and further discussed below.
3.4.1. General control capabilities
Controls regulate how supply, storage and DSM technologies meet
loads by determining the control logic and constraints applied. A simple
community scale system control strategy can include: (i) an order of
dispatch for the dierent resources, and (ii) a set of constraints.
3.4.1.1. Operational optimisation. Operational Optimisation (OO)
control is where the tool optimises, at each time step, the order of
dispatch of supply, storage, and DSM technologies to satisfy an
objective function which may relate to cost, emissions, etc. There are
dierences in detailed logical implementation between tools; a general
description is given here.
Most tools use the OO control chronologically i.e. calculations are
performed at each individual time step to establish an optimum based
on prevailing conditions at that time step only, before the next time step
is then considered. Storage is generally charged and discharged when it
is deemed favourable to do so according to the specic logical im-
plementation and objective function. Typically charging will occur
when there is excess energy from renewable or non-modulating supply
where storage is deemed to have benet over export or curtailment, or
where grid parameters, e.g. tari, make charging from grid advanta-
geous. Discharge from available storage is generally treated as a dis-
patchable supply option. The value attached to storage charge and
discharge takes account of characteristics of the storage system, e.g.
eciencies and costs, plus parameters such as taris and carbon con-
tents. For example, in HOMER the discharge energy cost includes
average charge energy cost, eciencies, and battery wear, lifetime and
replacement costs.
OO control is applied non-chronologically in some tools e.g. in
EnergyPRO the whole calculation period is scanned for energy supply
costs and an optimised supply schedule determined, with excess low
cost generation charging storage and discharge occurring to meet de-
mand in subsequent favourable high cost time steps. These OO control
functionalities may replicate real control systems for situations where
local renewable consumption is prioritised or where a set taristruc-
ture is established for energy import and export; the non-chronological
OO implementation may in some circumstances provide a somewhat
optimistic view of system performance as perfect foresight is implied.
3.4.1.2. Fixed order. Fixed Order (FO) control is where there is an
available set of functions with pre-dened order of dispatch of supply,
and xed conditions for the use of storage and DSM technologies.
Dispatchable supply is dispatched in a xed order in periods where non-
dispatchable, typically renewable, supply is below demand.
EnergyPLAN, H2RES, and Merit charge electrical storage in periods of
excess renewable production and prioritise discharge from electrical
storage over generators and power plants. In Merit thermal storage
discharge is prioritised over other thermal supply options. In
EnergyPLAN thermal storage charging is prioritised to absorb excess
electricity or heat production and discharged to avoid non-renewable
generation. In iHOGA batteries can charge/discharge at xed, user
input tarivalues. In the Biomass decision support tool excess heat
from the biomass boiler is stored in a thermal storage and discharged
when demand exceeds supply. EnergyPLAN includes several selectable
functions for dealing with excess electricity production. Hybrid2
contains embedded functionality for 13 pre-dened xed order
controls relating to the practical performance of electric systems
(Manwell et al., 2006).
3.4.1.3. User-dened order. User-dened Order (UO) control is where
the order of dispatch, for at least some part of the supply, is dened by
the user. For example, UO in EnergyPRO requires all supply options to
be given an order of preference, which can also include separate
priorities for production to satisfy dierent (peak, high, low) loads;
storage priority setting is not an option and in this tool storage
operation always follows the OO control strategy.
3.4.1.4. Modulating output. Modulating output (MO) control applied to
a dispatchable supply allows modulation of output to match load above
some minimum supply output level. In all tools the grid connection, if
enabled, can modulate output to follow electrical load with a minimum
supply level of zero. HOMER can only designate grid or generator
supplies to this control while in EnergyPRO, DER-CAM, and eTransport
any dispatchable supply can be assigned.
3.4.1.5. Non-modulating output. Non-modulating output (NO) control
sets the constraint that a designated supply must run at a xed output
whenever it is running. In the Biomass decision support tool, the
designated supply is the biomass boiler. In EnergyPRO the user selects
supplies. In iHOGA and HOMER the designated supplies are the
generators. In these two tools a set state of charge for storage can be
specied and the designated supply will continue operating, regardless
of availability of renewable generation, until the set point is reached.
This mimics a common feature in real systems used to maximise battery
life but which reduces the potential for renewable inputs to the store.
3.4.1.6. Advanced control. HOMER oers the capability to use
Advanced Control (AC) strategies where users can dene more
complex control operating regimes than those previously outlined by
interfacing with externally written code in MATLAB (HOMER Energy,
2017a).
3.4.2. DSM related control capabilities
The general control modelling capabilities described in the previous
section, such as OO and FO, can be used where there is storage in the
system to capture DSM functionality associated with storage charging
and discharging. Several tools have further DSM specic functionality
to represent Load Shifting,Demand Curtailmentand Electrical
Vehiclesin the system. All DSM related control capabilities are cap-
tured in the DSM controlcolumn of Table 4, the further DSM specic
functionalities are described below.
3.4.2.1. Load shifting. Load shifting (LS) is where a exible load is
dened which can be met or deferred to a later time step within a
limited deferrable time period, while incurring no loss. The exible
load can be input as a specic energy quantity over the deferrable
period in EnergyPLAN which uses 1 day, 1 week, or 4 weeks deferrable
periods, and in Hybrid2 which allows users to input the deferrable
period. In DER-CAM the exible load is sized as a percentage of the
main load over a 1 day deferrable period. The exible loads in these
tools are actuated when lowest cost or surplus energy is available
within the exibility period. HOMER and Hybrid2 can accommodate
more detailed model parameters such as: average deferrable load
(kWh/day), capacity (kWh), peak load (kW), and minimum load
ratio, exible load in these tools is treated as secondary to the main
load but prioritised over charging storage.
3.4.2.2. Demand curtailment. Demand curtailment (DC) is where
demand can be curtailed under certain conditions, and, unlike load
shifting, is not shifted but reduced. DER-CAM is the only reviewed tool
capable of modelling DC and curtails demand when tariprices exceed
a user dened curtailment cost (£/kWh) within an annual maximum
number of curtailment hours. There is also additional functionality to
allow for up to 5 daily hourly proles capturing the proportions of the
main load which can be curtailed at each time step.
3.4.2.3. Electric vehicles. Electric vehicles are going to play a vital role
in the future of energy systems (Cazzola et al., 2016;Urban Foresight
for Transport Scotland, 2016), and there has been research into the
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system exibility they can provide (García-Villalobos, Zamora, San
Martín, Junquera, & Eguía, 2015;Navigant Research, 2017). Only two
of the identied tools include models for an electric vehicle to grid
interaction. EnergyPRO has a model based on the energetic capacity of
the batteries in the cars, and limits on the charging and discharging
along with associated eciencies. The demand for the vehicles is input
as a time series and there are options accounting for availability.
Charging/discharging can be set to on/owith charging allowed at
zero demand, it can be set to proportional to the driving demand time
series, or it can be set its own time series. EnergyPLAN contains a
similar model. The inputs are for maximum discharge/charge, capacity
of batteries in vehicles, eciencies, and a time series for demand.
Simpler assumptions are made on the availability, with the fraction of
cars driving at peak demand and of cars parked used to calculate the
connection of cars to grid.
3.5. Storage modelling capabilities and underlying models
This section looks at relevant capabilities of the 13 screened tools
and underlying models with respect to storage functionality. Such
functionality enables DSM and, in the reviewed tools, is used with the
operational optimisation and xed order controls (see Section 3.4.1).
Storage capabilities are captured in two look up tables for use in tool
selection. Table 5 describes the range of storage modelling capabilities
available in each tool, with more detailed descriptions of these cap-
abilities in the sub-sections below. Table 6 gives a summary of the more
advanced models i.e. more detailed models than the simple storage
model (SSM) for each storage technology; SSM can be used to model all
storage types and is not included in Table 6 for this reason. A brief
summary of each capability and underlying model is given below,
further details including model equations can be found in the relevant
References
3.5.1. Electrical storage modelling capabilities and underlying models
Electrical storage is a general term used here to include electro-
chemical (li-ion, ow, lead-acid batteries), electromagnetic (super-
capacitors), and mechanical (CAES, hydro, ywheels) forms. Electrical
storage can be represented using a number of dierent mathematical
models, the dierent models used in the tools are categorised and de-
scribed below. The level of detail required at the planning stage de-
pends on the specics of the system being modelled and the outputs to
be derived from the modelling.
3.5.1.1. Simple storage model. A tool possessing a Simple Storage Model
(SSM), which can interact with supply and load, can model any storage
technology. EnergyPLAN and EnergyPRO use the SSM to dene all
types of storage, including all electrical storage types. iHOGA, DER-
CAM and HOMER support the use of the SSM, e.g. for high-performance
batteries (Lambert et al., 2006). HOMER also recommends its use for
simple pumped hydro storage systems. The SSM consists of a simple
energy in/out balance via an energy store. Energy can enter the store
below a threshold maximum charging rate up to a maximum store
capacity. There can be self-discharge from the store e.g. a percentage or
other function at each time step. Energy can leave the store below a
threshold maximum discharging rate. For charging and discharging
there are associated eciencies, which combine with self-discharge to
give a round-trip eciency. Charge and discharge eciencies are both
generally xed values. The SSM has xed maximum charge and
discharge rates independent of the state of the system, this
approximation may be sucient for some analyses, but may not be
realistic in other cases, more detailed models are available. Storage
lifecycle analysis is included in some tools with the SSM, e.g. in HOMER
lifetime is modelled as both an energy throughput and time, however
performance degradation eects are only included in the MKiBaM
model described later.
3.5.1.2. Kinetic battery model. The Kinetic Battery Model (KiBaM) was
rst developed for modelling lead-acid batteries in hybrid energy
systems (Manwell & McGowan, 1993). It is described as a two tank
model (HOMER Energy, 2016), where one tank holds the available
energy to directly support charge and discharge and the other holds the
bound energy which transfers energy to and from the available tank
according to a dened exchange function representing the chemical
process. The model supports charge/discharge rates as functions of
stored energy in the two tanks. The underpinning electronic
mechanisms are still somewhat simplied with voltage modelled only
as a linear function of energetic state etc. iHOGA and HOMER both
possess this model and have libraries of electrochemical batteries with
parameters established from test data.
Table 5
Storage modelling capabilities and underlying models.
Tools Electrical
storage
Thermal
storage
Fuel
synthesis
Fuel
storage
Biomass decision
support tool
No MB No B
COMPOSE KiBaM CS, SSM No No
DER-CAM FB, SSM MB No No
EnergyPLAN CAES, PH, SSM SSM, STS BF, BG, EF,
GtL, H
G, O, M
EnergyPRO PH, SSM CS, MB BF, BG, EF,
GtL, H
G, O, M
eTransport Yes
a
Yes
a
Yes
a
Yes
a
H2RES Yes
a
Yes
a
No Yes
a
HOMER FB, KiBAM,
MkiBaM, PH,
SSM
No H H
Hybrid2 EKiBaM No No No
iHOGA KiBAM,
MKiBaM, SSM
No H H
MARKAL/TIMES Yes
a
Yes
a
Yes
a
Yes
a
Merit EKiBaM SSM No No
SimREN Yes
a
No No No
Key:
Electrical: Compressed air energy storage model (CAES); Extended kinetic battery model
(EKiBaM); Flow battery model (FB); Kinetic battery model (KiBaM); Modied kinetic
battery model (MKiBaM); Pumped hydro model (PH); Simple storage model (SSM).
Thermal: Cold storage model (CS); Moving boundary model (MB); Seasonal thermal
storage model (STS); Simple storage model (SSM).
Fuel synthesis: Biofuel (BF); Biogas (BG); Electrofuel (EF); Gas to liquid (GtL); Hydrogen
(H).
Fuel storage: Biomass (B); Gas (G); Hydrogen (H); Methanol (M); Oil (O).
a
indicates that the tool has a certain capability but specic models used were not able
to be conrmed; these tools were assumed to have SSM as minimum electrical and
thermal storage models.
Table 6
Electrical and thermal storage technologies and advanced models (beyond SSM).
Electrical storage
(ES) type
Advanced ES
models used
Thermal storage
(TS) type
Advanced TS
models used
Lead-acid battery EKiBaM, KiBaM,
MKiBaM
Hot water tank MB
Li-ion battery EKiBaM, KiBaM,
MKiBaM
Cold storage CS
Flow battery FB Seasonal thermal
storage
STS
Pumped hydro PH
CAES CAES
Key:
Electrical: Compressed air energy storage model (CAES); Extended kinetic battery model
(EKiBaM); Flow battery model (FB); Kinetic battery model (KiBaM); Modied kinetic
battery model (MKiBaM); Pumped hydro model (PH); Simple storage model (SSM).
Thermal: Cold storage model (CS); Moving boundary model (MB); Seasonal thermal
storage model (STS); Simple storage model (SSM).
Note: SSM can be used to model all storage types and is not included.
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3.5.1.3. Extended kinetic battery model. Work was done to improve the
KiBaM in terms of modelling voltage behaviour (Manwell & McGowan,
1994). These models are denoted here as Extended Kinetic Battery
Models (EKiBaM). Hybrid2 includes such an improved model (Manwell,
McGowan, Abdulwahid, & Wu, 2005), with voltage, charging and
discharging eciencies and current as non-linear functions of the
state of charge. Merit also contains a dierent but similar model with
improved voltage modelling (Born, 2001).
3.5.1.4. Modied kinetic battery Model. A further Modied Kinetic
Battery Model (MKiBaM) is used by HOMER and iHOGA to give
deeper insights. This includes a thermal model component whereby
the resistive properties of the battery produce heat which aects
temperature, capacity and lifetime. Secondly, it involves cycle-by
cycle degradation of the battery as a function of depth of discharge;
this is accounted for using the Rainow counting algorithm (Downing &
Socie, 1982), which iHOGA also further utilises to account for corrosion
eects over time. iHOGA oers customised models for lead-acid
batteries (Copetti & Chenlo, 1994;Schier et al., 2007) and Li-ion
batteries (Groot, Swierczynski, Irina, & Knudsen, 2015;Saxena,
Hendricks, & Pecht, 2016;Wang et al., 2011).
3.5.1.5. Flow battery model. Flow batteries can also be modelled
explicitly with models which account for the independence between
capacity and charge/discharge and other ow cell characteristics. Flow
battery specic models based on manufacturers data are included in
DER-CAM (Stadler, Marnay, Siddiqui, Lai, & Aki, 2009) and HOMER
(HOMER Energy, 2016).
3.5.1.6. Pumped hydro model. Pumped hydro is often modelled using
the SSM by factoring in the capacity and eciency of the pump and
generator as well as the capacity of the reservoir. EnergyPLAN and
HOMER include pumped hydro as a technology using the SSM. Only
EnergyPRO includes an explicit pumped hydro model and includes
inputs such as reservoir volume, friction factors and head dierence.
3.5.1.7. Compressed air energy storage model. A simple compressed air
energy specic storage model (CAES) is included in EnergyPLAN, with
a focus on the economic trading possible (Lund & Salgi, 2009).
3.5.2. Thermal storage modelling capabilities and underlying models
Thermal storage allows for sensible or latent heat to be kept for
meeting a demand later. It can include hot water tanks, brick radiator
stores, phase change storage materials, and cold storages. It can also be
designed for buildings or community scales. A summary of dierent
thermal storage models including underlying equations is given by
(Dumont et al., 2016). The tools that are the focus of this paper use only
the least complex models, some of the limitations associated with this
are discussed later. The categorisation of thermal storage models found
in the tools is captured in Tables 5 and 6 and described below.
3.5.2.1. Simple storage model. The SSM model does not consider
temperatures but only accounts for energy, and was described earlier
in Section 3.5.1.1. EnergyPLAN uses the SSM to model all thermal
storage technologies.
3.5.2.2. Moving boundary model. The most common model for thermal
storage in the examined tools is the moving boundary model (MB),
where the additional inputs over the SSM are top and bottom tank
temperatures. It assumes that there is no mixing between the upper hot
zone and the lower cold zone and the thermocline boundary layer is
innitesimally small. This is again an energy balance model with
inows and outows of energy moving the boundary layer up and
down the store and stored energy calculated based on the thermocline
position. The model does not explicitly capture temperature variation
due to losses and destratication. This model is incorporated in the
Biomass decision support tool, DER-CAM, EnergyPRO, and Merit. The
model can be adjusted in EnergyPRO using a utilisation factor which
reduces the useful energy which can be used for supply. DER-CAM
allows for dierent high temperature and low temperature stores within
the system to allow for dierent heat generation devices (Steen et al.,
2015). EnergyPRO also uses the MB model for cold storage (CS) and
was the only tool identied to have electrical, heat, and cold storage
modelling capability.
3.5.2.3. Seasonal thermal storage model. A seasonal thermal storage
model is included in EnergyPLAN. It is simplied and only two inputs
are required: capacity, and days of optimising storagewhich allows for
the model to identify inter-seasonal variations in demand. (Allegrini
et al., 2015) set out the state of art in modelling seasonal thermal
storage in building-scale simulation tools, but in general this
functionality is not supported in the tools analysed here apart from
EnergyPLAN.
3.5.2.4. Other thermal storage models. Temperature variations, and
therefore entropy considerations, are vital in real thermal storage
analysis (Bejan, 1978). There may appear to be enough energy in a
tank to meet the energy demand, but if the temperature does not meet
the supply requirement it is not useful energy. The MB model does not
account for changes in the temperature zones; there are no entropic
considerations. The (Dumont et al., 2016) summary of modelling
approaches for sensible thermal storage tanks includes the MB model
and highlights the models which would be used to include entropy,
with increasing detail at the expense of computational and data input
complexities.
3.5.3. Modelling of fuel synthesis and storage
Fuel synthesis is the production of fuels within a system creating a
new energy vector which can be used across a range of energy sectors,
and acts as storage to be used later (Ridjan, Mathiesen, Connolly, &
Duić, 2013). EnergyPLAN, iHOGA and HOMER can model the synthesis
of hydrogen. This is produced using electricity with an electrolyser to
form hydrogen, stored in a hydrogen tank, and then converted to meet
transport, heat, or electricity demands. All three technical components
can be modelled within the three tools. EnergyPRO contains a simple
model for the synthesis of any fuel. EnergyPLAN allows for synthesis of
dierent types of fuel: biofuel, biogas, hydrogen from electrolysis,
electrofuel, and gasication to liquid transport fuel. These fuels are
used to form interactions between energy sectors, and ensure high-
value energy is used for high-value processes.
These fuels must then be kept in storage. The Biomass decision
support tool can size biomass fuel storage, while iHOGA and HOMER
can model hydrogen storage tanks. EnergyPLAN can model gas, oil and
methanol storages, and EnergyPRO can model any fuel storage as a
generic model.
3.6. Practical considerations
This table sets out practical considerations associated with selecting
a tool: cost, access, support, whether it is academic or commercial, user-
friendliness, and whether there is existing available expertise.
Cost may be a vital factor in choosing an energy system tool and
depends on the resources available to a user. A student is likely to
choose a free tool which there is abundance of: Biomass decision sup-
port tool, COMPOSE, DER-CAM, EnergyPLAN, iHOGA, Hybrid2, Merit
and MODEST. Often tools are available at discounted prices for stu-
dents. A government agency or an engineering consultancy may have
the resources available to aord the cost for a tool such as 3000+ EUR
for EnergyPRO, 5001500 USD for HOMER, or 12753130 EUR to
manipulate the code for MARKAL/TIMES.
Accessibility is dened in terms of availability, purchase require-
ment, and if the tool was downloadable or browser-based. Available
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support as indicated by tool websites and veried by the authors is
listed, and includes: user manual, available contact details, videos,
training, and an online forum. The tools are classed as academic or
commercial based on the development and ownership of the tools
through either a university/research group, or a private company, re-
spectively.
User friendliness was judged on the provision of an intuitive model-
building pathway which was subjectively graded by the authors at a
low, medium, or high level. This required rst-hand knowledge of the
tools so where the tool was not available to the authors, the grade by
(van Beuzekom et al., 2015) was referenced.
Most modelling tools require a signicant investment in time to
develop expertise in order to be used correctly and prociently so there
will be a strong practical driver to use a modelling tool which has es-
tablished available expertise if this exists. If there is no established
expertise available and the aim is to develop such an expertise then this
driver will be less strong or zero.
4. Tool selection process
A stepwise tool selection process was developed to aid in the
identication of an appropriate tool for a particular analysis for plan-
ning-level design of a community energy system incorporating storage
and DSM, based on the process of Sandia National Laboratories (Lin
et al., 2007).
4.1. Determination of requirements
The rst process step is to establish which of the modelling tool
capabilities (documented in Tables 27) are essential,desirableor
not applicableand to assign values of 2, 1, and 0 respectively to each
of these tool capabilities. This process requires that each of the cap-
abilities described in the column headings and associated keys of the
tables are individually considered against the requirements for the in-
tended analysis. For example if we look at Table 2 then the three tool
capabilities captured are demand prole generator,resource assessor,
and supply prole generator; if the user requires the tool to provide
demand proles, weather data and renewable generation supply pro-
les from simple input data such as location and demographics then
these capabilities would be considered essential and each of these
capabilities would be assigned a value of 2; alternatively if the user has
available data for demand, weather and renewable generation and
supply (e.g. from monitored data) then these capabilities are not
applicable so would be assigned a value of 0 and can be eliminated from
further consideration; if the user can potentially source information and
generate the demand, weather and renewable generation input data but
this would be signicant eort then this capability could be ranked as
desirable and allocated a value of 1. Similarly, if we consider Table 3 it
may be that it is essential that there is capability to model electrical
generation with both PV and wind so each of these capabilities would
be allocated a 2 while if there is no potential for hydro then this cap-
ability would be allocated a 0. When this process is complete the es-
sential and desirable capability requirements have been established.
The rst 4 rows of Table 8 illustrate this process for a simple case study
example which will be described in more detail in the following section.
4.2. Scoring of tools against requirements
Once the requirements have been established then each of the tools
can be scored against them. The rst consideration is whether all the
essential capabilities are available, if a given modelling tool has all the
essential capabilities it can be considered further, those which do not
pass this check can be discounted. For the tools which pass, their scores
for the essential plus desirable capabilities are summed into an overall
score and ranked with the most suitable tools having the highest scores.
Again, Table 8 illustrates this process for the simple case study which is
described in more detail in the following section.
4.3. Example application of the modelling tool selection process
Findhorn is an ecovillage in the north-east of Scotland with an
ambition to transition to a local, low-carbon energy system. It consists
of around 75 buildings, with a private wire electrical network, wind
and solar generation, a grid connection, micro-district heating from
biomass, and individual household heat pumps and solar thermal sys-
tems. The community could be said to be net zero carbon but has large
electricity surpluses and shortfalls due to stochastic demands and re-
newable production. The community have an interest in the use of
thermal and electrical storage with advanced controls as a potential
route to achieving their aims. The community had previously been
monitored as a research and demonstration site for advanced DSM
(Tuohy et al., 2015).
The community overall objective is to increase their energy au-
tonomy and use of local renewable energy resources; they have some
concerns over the sustainability of biomass. To help achieve their ob-
jective they enlisted support from a University and after an initial
Table 7
Practical considerations.
Tools Cost Access Support Academic/
Commercial
User friendly Available
Expertise
Biomass decision support
tool
Free Download User manual, videos, online course Commercial High Yes/No
b
COMPOSE Free Download Videos, forum Academic Med Yes/No
b
DER-CAM Free Browser User manual, videos, forum Academic Med Yes/No
b
EnergyPLAN Free Download User manual, contact, videos,
training, online course
Academic High Yes/No
b
EnergyPRO 3000+ EUR for all modules Purchase User manual, contact, training Commercial High Yes/No
b
eTransport Not available Not available Not available Academic High
a
Yes/No
b
H2RES Not available Not available Not available Academic Not available Yes/No
b
HOMER Free 2-week trial, 5001500 USD Purchase User manual, contact, videos,
forum
Commercial/
Academic
High Yes/No
b
Hybrid2 Free Download User manual, contact Academic Not available Yes/No
b
iHOGA Educational Free, 500 EU for 1 year Purchase User manual, forum, contact Academic Med Yes/No
b
MARKAL/TIMES Costs 12753130 EUR to
manipulate source code
Download User manual, paid support, forum Academic Low
a
Yes/No
b
Merit Free Download Training Academic Med Yes/No
b
SimREN Not available Not available Not available Commercial Not available Yes/No
b
a
From (van Beuzekom et al., 2015).
b
User to self-assess.
A. Lyden et al. Sustainable Cities and Society 39 (2018) 674–688
683
scoping process identied two initial future illustrative scenarios to be
investigated: 1) increased electrical generation plus battery storage, and
2) increased electrical generation plus heat pumps and large hot water
tanks replacing the micro-district biomass heat source. The modelling
tool selection process was then applied in order to identify suitable
software to use for the investigation.
The rst step was to review the tool capability requirements:
Demand prole generator, Resource assessor, and supply prole mod-
eller capabilities (Table 2) were all deemed to have zero value (i.e. not
applicable) since multi-year sub-hourly data was readily available from
monitoring.
Electrical supply technologies wind, grid, and solar PV were deemed
to be essential (Table 3). Thermal supply modelling of fuel boiler
(biomass fuel in this case) and heat pumps were deemed essential.
Capability to model solar thermal and district heating in detail were
scored desirable but not essential at this stage as the primary focus was
on the electrical supply system and the available monitoring data in-
cluded heat delivery from existing heat production units, net of solar
inputs, and distribution losses.
Design optimisation capability (Table 4) was deemed desirable but
not essential as the view was taken that the relatively simple range of
options to be investigated could be covered through a full factorial
deterministic investigation and modelling outputs analysed outside of
the tool to establish potential optima. The output of hourly data al-
lowing autonomy, emissions, or renewable penetration to be estab-
lished was deemed essential, this level of system performance para-
meter output would then allow the other required outputs to be
calculated outside of the tool.
For control capabilities (Table 4) either FO or OO control was
deemed essential to support the required ordering of dispatch of supply
and storage, in addition to the MO control inherent in all the tools for
representing the grid. DSM specic control functionality was not re-
quired in this example.
Storage modelling capability was deemed essential for both elec-
trical and thermal storage (Tables 5 and 6). It was deemed that the
simple storage model was sucient but that it would be desirable for
more complex models to be available. Fuel synthesis and fuel storage
are not required in this simple illustrative study.
These technical requirements are captured (in the top 4 rows of
Table 8) and then each of the tools assessed against these requirements,
where a tool has an essential or desirable capability then it scores 2 or 1
respectively against that capability, otherwise it scores 0. Once all the
potentially capable tools have been assessed they are ranked: (i) rst
the tools which do not have all the essential are deemed to failto meet
the essential requirements and discounted and only those that Passthis
test considered further, (ii) the remaining tools are then ranked based
on their cumulative score. This process is illustrated in Table 8, with the
result in this case that 6 tools are capable with similar scores of either
20 or 21.
This example has been kept relatively simple for reasons of clarity
and brevity; more complex situations would follow the same process.
5. Discussion
Through the categorisation and documentation of tool capabilities it
is apparent that there are many dierences between tools. Some tools,
such as EnergyPLAN, combine all energy sectors based on the view that
holistic consideration across sectors leads to optimal solutions. Other
tools are primarily single domain focussed, e.g. iHOGA has strong
capabilities for electrical analysis with a wide range of storage models
but no thermal capability.
Design optimisation capabilities in the tools generally optimise for
nancial or technical considerations. Only iHOGA optimises for human
considerations (human development index, job creation) and two tools
optimise for environmental considerations. Much work has been done
on external optimisation used in a two-step process. This may inuence
Table 8
Output from application of tool selection process.
Essential
Capabilities
Overall Score Design
optimisation
Outputs Controls and
DSM
Supply technologies Storage
All essential
capabilities
met
Score (essential
+ desirable)
Yes Autonomy,
emission, or RES
FO or OO WT PV Fuel
boiler
Grid District
Heating
Solar
Thermal
Heat
Pumps
Electrical
Battery SSM
Electrical
Battery > SSM
Hot
water
tank
SSM
Hot water
tank > SSM
D = Desirable,
E = Essential
DE EEEEEDDEED ED
Value 1 2 2 2 2 2 2 1 1 2 2 1 2 1
COMPOSE Pass 21 1 2 2 2 2 2 2 0 1 2 2 1 2 0
DER-CAM Pass 21 1 2 2 2 2 2 2 0 1 2 2 0 2 1
EnergyPRO Pass 21 0 2 2 2 2 2 2 1 1 2 2 0 2 1
EnergyPLAN Pass 20 0 2 2 2 2 2 2 1 1 2 2 0 2 0
MERIT Pass 20 0 2 2 2 2 2 2 0 1 2 2 1 2 0
MARKAL/TIMES Pass 20 1 2 2 2 2 2 2 0 1 2 2 0 2 0
eTransport Fail 16 1 2 2 0 0 2 2 1 0 2 2 0 2 0
H2RES Fail 16 0 2 2 2 2 2 2 0 0 0 2 0 2 0
HOMER Fail 16 1 2 2 2 2 2 2 0 0 0 2 1 0 0
iHOGA Fail 14 1 2 2 2 2 0 2 0 0 0 2 1 0 0
Biomass decision
support tool
Fail 11 1 2 2 0 0 2 0 1 0 0 0 0 2 1
Hybrid2 Fail 9 0 0 2 2 2 0 0 0 0 0 2 1 0 0
SimREN Fail 6 0 0 0 2 2 0 0 0 0 0 2 0 0 0
A. Lyden et al. Sustainable Cities and Society 39 (2018) 674–688
684
the lack of embedded optimisation options in the tools, another factor is
the preference for the simplicity and transparency available in full
factorial parametric analysis.
The review identied a lack of detailed district heating modelling
capability in any of the community-scale tools, with only a heat loss
parameter as input, factors such as the heat demand density, distribu-
tion temperatures, network layouts and controls which have a large
eect on ancillary energy use and losses in district systems are not di-
rectly addressed.
Analysis of controls modelling capabilities in the tools showed a
wide range including operational optimisation, xed order, and user-
dened orders, for dispatch of supply and storage. Operational opti-
misation control is usually used with a cost based objective function,
other possible objective functions such as maximising local use of re-
newable generation, minimising grid imports or minimising emissions
are not generally directly supported, with DER-CAM a notable excep-
tion. More advanced predictive controls based on weather forecast and
demand prediction are not supported, although the non-chronological
operational optimisation in EnergyPRO and the deferrable load func-
tionality in HOMER etc. can represent this type of control but with
signicant simplications. The option to run tools in combination with
external control algorithms in separate software packages is one way
round this limitation.
The tools, with the exception of DER-CAM, focus on load shifting
and use of storage where there is grid connection to optimise value
based on cost (arbitrage) while it is widely accepted that other grid
services (such as frequency stabilisation, peak reduction, avoidance of
capital investments etc.) may also be very important.
The review of storage functionality and modelling revealed frequent
use of the simple storage model. More complex models for electro-
chemical storage exist particularly for use with lead-acid, li-ion and
ow batteries. Thermal storage is limited to simple energetic models
which do not directly take account of temperature variations other than
in assessing capacity. These may be suitable for initial planning design
stages but have limitations. To take account of temperatures, heat
transfer rates, stratication, and phase change in thermal stores ne-
cessitates more complex models. It would appear that these will be
required in the future to support realistic modelling of the hybrid sys-
tems and advanced controls for which these parameters have critical
importance.
There were few tools found to be directly capable of analysing fuel
synthesis technologies, such technology, however, is currently unlikely
to be at a community scale in the short term. For this reason tools de-
veloped for regional scale have most capability.
The wide range of tools available and their diering capabilities
makes a capability categorisation and tool selection process of value to
the end user of such tools, and also of use to inform those looking to
expend eort or resources in modelling of such systems. The abundance
of available tools and rapidly developing eld dictated that it was im-
possible to include every one. The authors believe their selection is
however reasonably representative of the state of the art in tools for
planning-level design at community scale.
The categorisation and selection process presented is not limited to
the tools identied here but is intended to provide a framework which
can be used in future to refresh the capabilities categorisation or be
applied to further tools. The review of required capabilities as the rst
part of the selection process can also form a guide for modellers to
ensuring relevant factors are considered. More detailed scoring systems
in the selection process would be possible, the pair-wise comparison
suggested by (Jong et al., 2011) remains to be investigated.
The tool selection process does not take into account the potential
for multiple tools to be used together to analyse the system under
consideration, such work is recommended for future studies. The more
detailed simulation modelling tools currently used in buildings and
systems domains have potential to be developed for community scale
energy systems in future, allowing more physical detail to be captured
in planning level design studies, their capabilities could also be assessed
and tools selected using the same process.
An element not considered here is the validation of the modelling
tools. So far in available literature case studies are largely based on
design and do not include monitored data on completed schemes that
include DSM and storage. Experience in the buildings industry has
found that performance gaps are common (Tuohy & Murphy, 2015a)
and identied that industry process needs to evolve to address these
gaps (Tuohy & Murphy, 2015b). It is critical that similar issues are
addressed to avoid performance gaps in future community scale energy
systems.
6. Conclusions
Future community systems will contain supply technologies reliant
on renewable sources which necessitate the inclusion of storage and
DSM. These need to be carefully designed to ensure they are resilient,
low-cost, and maximise use of renewable sources. Modelling is vital in
achieving these aims. This paper has screened 51 energy system ana-
lysis tools and identied 13 tools particularly suitable for planning level
design analysis of community systems with renewable energy and sto-
rage.
Tool capabilities were then categorised and documented in a series
of tables. A tool selection process, based around these tables, has been
developed capable of identifying an appropriate tool for a specic
analysis, and then illustrated for a case study.
The suitability and limitations of the selected tools were discussed,
and suggestions made for areas of improvement. Gaps were identied
particularly in the modelling of thermal storage systems and their
controls due to the use of simple energetic models which do not readily
capture important thermal characteristics such as temperatures.
Acknowledgements
This work was nancially supported by grants from the UK
Engineering and Physical Sciences Research Council (EPSRC). This work
was carried out within the context of IEA ECES Annex 31 Energy
Storage in Low Carbon Buildings and Districts: Optimization and
Automationand also within the context of the UK EPSRC project Fabric
Integrated Thermal Storage(FITS), grant number EP/N021479/1.
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Conference Paper
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There is a rising interest for optimal use of thermal energy storages (TES) in buildings to improve energy efficiency and for load shifting in demand side management. In this context, a state of the art of the different methods for simulating sensible TES is proposed. Mathematical equations which describe the processes occurring in a sensible TES are difficult to solve with a simple formulation. That is the reason why a large number of storage models have been developed in the last decades. Few studies compare the different modeling approaches and their respective advantages and limitations. A review of the literature is thus performed and it focuses on eight different modeling approaches. The comparison is performed in terms of computational time, accuracy and application. A tree of selection is proposed to select the optimal TES modeling method for a given application.
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