In an increasingly dynamic and changing electricity sector with rising distributed energy resources, new network investment models are needed that enable consideration of flexibility, uncertainty and risk. Existing modelling frameworks include “top down” models that offer a comparison of investment and investment strategies between scenarios and “bottom up” models that consider detailed technical impacts on real networks. These frameworks are broadly appropriate for analysing investment, with the requirements of the business, stakeholders and regulator influencing the specific model design and implementation. There are a number of dimensions across network engineering, investment, customers and energy markets to be captured and represented in the modelling at some level. This paper presents a number of advanced modelling techniques which can be applied to both topdown and bottom up modelling frameworks, enabling better consideration of customer variability, network risk and optioneering of solutions. Drawing on Bayesian statistics, customer load has been represented using a sophisticated statistical model that reflects both variability and uncertainty in demand on LV networks. This can help to explicitly quantify network risk due to existing loads, new loads and customer flexibility. A network ‘emulator’ model provides significantly faster run- times for analysis of large solution sets by parameterising the variables of a power flow model against the inputs. This has been applied and tested with LV, HV and EHV networks with results closely matching equivalent power flow models. Implemented in combination with the Bayesian customer load model enables probabilistic, risk- based modelling. A constrained cost optimisation algorithm has also been developed to find the lowest cost

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Summary
In an increasingly dynamic and changing electricity sector
with rising distributed energy resources, new network
investment models are needed that enable consideration
of flexibility, uncertainty and risk. Existing modelling
frameworks include “top down” models that offer a
comparisonofinvestmentandinvestmentstrategiesbetween
scenarios and “bottom up” models that consider detailed
technical impacts on real networks. These frameworks
are broadly appropriate for analysing investment, with
the requirements of the business, stakeholders and
regulator influencing the specific model design and
implementation. There are a number of dimensions across
network engineering, investment, customers and energy
markets to be captured and represented in the modelling
at some level. This paper presents a number of advanced
modelling techniques which can be applied to both top-
down and bottom up modelling frameworks, enabling
better consideration of customer variability, network risk
and optioneering of solutions.
Drawing on Bayesian statistics, customer load has been
represented using a sophisticated statistical model that
reflects both variability and uncertainty in demand on LV
networks.This can help to explicitly quantify network risk
duetoexistingloads,newloadsandcustomerflexibility.A
network‘emulator’modelprovidessignificantlyfasterrun-
times for analysis of large solution sets by parameterising
the variables of a power flow model against the inputs.
This has been applied and tested with LV, HV and EHV
networks with results closely matching equivalent power
flow models. Implemented in combination with the
Bayesian customer load model enables probabilistic,
risk- based modelling. A constrained cost optimisation
algorithm has also been developed to find the lowest cost
combination of solutions that can address constraints that
have arisen on a network. This is based on a cost function
that accounts for factors including disruption costs, cross-
networkbenefits,lifeexpectancy,enablercosts,flexibility,
as well as capex and opex.
Finally, it is important that these models represent the
reality of the actual network planning and investment
decision making processes they are representing. This
is often very complex, and can be difficult to reflect in a
model. Alternatively, decision-making processes may be
adapted to incorporate them.
1. Introduction
Network investment modelling is a core element of
business planning for distribution networks. Business
planning is generally driven by regulatory price controls
orsimilarwiththebusinessplanthendelivered,monitored
and updated over the price control period. This includes
both load-related and non-load related investment
(although in this paper, we only consider the modelling of
load- related investment).
The modelling of load related investment is typically
broken down into the following elements at a high level:
constraints under future load scenarios
corresponding investment requirements for those
interventions, at scale across an entire distribution
network area
Essentially, the modelling process aims to reflect
the network planners decision-making processes for
Advanced techno-economic modelling
of distribution network investment
requirements
C. E. HIGGINS*, G. McFADZEAN, G. EDWARDS
TNEI Services Limited
United Kingdom
KEYWORDS
Distribution planning, distributed energy resources, risk, novel modelling techniques, electric vehicles
* charlotte.higgins@tneigroup.com

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71
level, or for large regions. But, for distribution networks,
it is necessary to disaggregate these scenarios down to a
much more granular level. Achieving this at a granular
enough level for accurate LVmodelling is challenging and
any estimate of this is likely to be uncertain.
Characterisation of Network Performance
Transmission networks are, almost by definition, much
smallerinscaleintermsofthenumbersofcircuitsandtheir
extent. For example, the Great Britain (GB) transmission
network includes approximately 24,000 km of overhead
line and cable, with typical lengths of around 20km. In
contrast, distribution networks typically comprise 10,000s
if not 100,000s of substations. The GB distribution
network includes ~800,000 km of overhead lines and
cables.Asignificant proportion of this is low voltage (400
V)network,including,byoneestimate,almostonemillion
low voltage feeders, which tend to be less than a kilometre
in length. This is split across fourteen distribution licence
areas, with an average of around 57,000 km of circuit per
network in detail i.e. as a whole power system model, is
clearly not practical.
Distributionnetworkbehaviouristhus,generallymodelled
at a greater level of detail at higher voltage levels (e.g.
using power flow models) where network security is more
critical and at a more conceptual or representative level
for lower voltage networks where volumes are higher
and criticality is lower on an individual network basis.
Network characterisation at a minimum should enable the
identification of constraints i.e. investment triggers (due
to thermal loading, voltage and, in some cases, fault level
and harmonics) the minimum capacity required from any
network interventions, and allow for robust scaling up of
investment requirements to licence area level.
For distribution networks, there are also challenges due to
the lack of planning and operational data. Data at higher
voltage levels tends to be of a reasonably high quality,
comparable with similar data about the transmission
network. However, at lower voltage levels, the quality and
quantity of data tends to decline. This makes distribution
network models more challenging to build and verify.
Thisisaparticularlyprofoundproblemasthecostimpacts
of heat and electrification are likely to be more significant
for the LV network. Smart meters can help to fill in gaps
where available, but there are open questions about how
investment, but on a large-scale and coherently across
the business. In practice, this decision-making process is
often nuanced and complex and may rely on a planner’s
experience of a specific network. This can be difficult
to capture fully in a model. It is described in more detail
below.
Future Load Scenarios
There is considerable uncertainty about the long-term
evolution of the world’s energy systems. The interlined
trends of decarbonisation, decentralisation and digitisation
are widely recognised, however, the exact manner in
which these will impact the energy system is very unclear.
For example, many people expect widespread adoption of
electric vehicles to happen in the future, but the scale and
the pace of this adoption is not clear. Furthermore, it is
credible that other technologies (e.g. hydrogen fuel cell)
cars could displace EVs in the future, which would have a
much less significant impact on the networks.
Therefore, planners often define long-term scenarios
for electricity supply and demand, that consider a range
of credible future demand and generation. This enables
a consistent and coordinated assessment of investment
needs across both transmission and distribution. These
as electric vehicles and heat pumps, evolving network
interventions and, increasingly, distributed flexibility
trends such as energy storage and demand side response.
One of the emerging challenges is the modelling of new
loads such as EVs and HPs where there is little available
data to date on patterns of usage and how they interact
with existing loads and between themselves. Various
street parking. However, in total this amounts to less than
500recordsforslowchargers,coveringaroundayeareach.
It’s similar for public datasets that describe the behaviour
of residential off-street fast chargers. Although this will
change in future, particularly with the development of
the internet of things and the roll-out of smart meters,
business planning decisions may need to be taken before a
substantialamountofdataisavailabletoinformmodelling
of these loads.
Inaddition,scenariosaretypicallydefinedatacountry-wide

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traditional network asset solutions, they are less costly and
faster to deploy and provide more optionality for future
network planning and investment.
However, the capacity provided by, and cost of, flexibility
solutions are both much more uncertain compared to
traditional network assets. For example, they may rely on
a customer behavioural response (even more automated
responses rely on certain conditions to be present) or
response of intermittent generation or energy storage. For
strategic modelling purposes, it might be reasonable to
assume that they can provide a certain “typical” level of
capacity to the network. However, the estimation of any
generalised capacity increase should be based on a more
nuanced understanding of (over-) procurement levels
requiredforvariousservicesandthelikelihoodofacertain
level of response.
Optioneering of network interventions both at a strategic
as well as business as usual level should consider the
lifetime capital and operational cost of the solution, as
well as the cost of any other “enabling” assets. It might
also be necessary to consider harder to quantify costs,
such as the cost of disruption or the optionality value of
network investment can be complex and far-reaching
beyond purely load related drivers. For example, asset size
can also be driven by losses and reliability. These should
be considered when assessing the value in novel techno-
economic modelling techniques.
2. Overview of existing modelling
Table 1 lists some of the dimensions that a techno-
economicmodelofdistributionnetworkinvestmentwould
be looking to capture at some level.
such data can be used to inform LV network performance
if incomplete or aggregated due to data privacy.
To date, characterisation of distribution network
performancehasgenerallybeenundertakenonthebasisof
themoststressfulnetworkconditionsi.e.peakdemandand
peak generation at summer minimum, with consideration
of network security requirements and validation against
suitable for new loads and evolving customer behaviours
on the network which may lead to greater diversity of
constraining conditions and how these might be best
managed, and may not help to understand year-round cost
drivers such as losses.
There is also an inherent randomness in how a customer
uses electricity, which is increasingly apparent at lower
voltage levels, and there are even striking differences
in the patterns of behaviour between customers that are
demographically similar. This uncertainty also applies to
intermittent renewable generation and flexibility solutions
such as demand side response which rely on a customer
in a more probabilistic way that accepts and characterises
theuncertaintyofbothexistingandmoreimportantly,new
loads, and interventions should enable an understanding
of network risk. However, this is only considered in
Modelling of Interventions and Investment
Requirements
“Smart” and no-network interventions are increasingly
beingconsideredasnetworkinterventionoptionsatarange
of voltage levels, particularly where there is significant
uncertainty around levels of future loading. Whilst these
interventions generally provide less capacity than more
Table 1 : Examples of techno-economic distribution network modelling dimensions
Network Engineering Network Investment Customer Energy Market
Voltage excursions
Thermal loading
Asset condition
Phase imbalance
Security of supply
Optionality
Flexibility
Economies of Scale
Variability between customers
Diversity
Elasticity
Variability in flexibility
services

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73
Workstream 3:
was developed which could simulate the expected
investmentrequirementsoftheGBdistributionnetwork
over long timescales (out to 2050) under a range of
future scenarios. This model accounted for technical
network impacts, energy and power usage by different
typesofcustomer,interactionswiththewiderelectricity
market, the investment processes and interventions
used by DNOs to address network issues, and wider
commercial aspects of a DNOs business (including
different investment strategies). The model was largely
implemented in Excel, without detailed analysis of
power flows on the distribution network. This is
therefore more of a “top-down” model, which is able to
consider a number of facets at a large scale, but not in a
huge amount of detail.
Workstream7:
models were built of four archetypal distribution
network topologies (e.g. urban, rural etc). The impact
of future technologies and scenarios on these networks
was studied in considerable detail, accounting for a
detailed investigation of a wide range of technical
phenomena. The implications of this were thoroughly
discussed,however,noattemptwasmadetoextrapolate
these impacts across the rest of the country, or to
translatetheseimpactsintocosts.Thisisthereforemore
of a “bottom-up” model, which considers one facet of a
small part of the network in a high level of detail.
MERGE:
evaluate of the impacts that EV will have on the EU
electric power systems regarding planning, operation
was performed for three EU countries (Spain, Portugal
and Greece) including various distribution networks
(rural, urban and touristic). The impact of EV presence
in system operation was analysed in terms of branch
congestion levels, energy losses and voltage profiles, in
a bottom up manner.
In order to address these dimensions and challenges,
models of the distribution network tend to take one of
several possible approaches, described in more detail
below.
Top-down models:
significant amounts of abstraction and simplification in
ordertotrytocapturethebroadevolutionofthedistribution
system. These sorts of models might enable a wider range
of “dimensions” to be considered – e.g. both technical
impacts and commercial/economic factors – but these
will, by necessity, have to be represented in the model in a
simpler manner.
Bottom-up models: These models provide a much more
thoroughanddetailedrepresentationoftheactualstructure
and dynamics of part of the system. However, because of
the complexity and scale of the problem, this means they
will tend to focus on looking in detail at a smaller number
of facets of the problem. For example, a bottom-up model
might study the technical impacts of a future scenario on a
small number of real parts of the network, but won’t try to
determine the cost or social impact associated with this or
toconsiderthevarietyofimpactsacrossanentirenetwork.
Simple models: For some applications a simple model
might suffice, which doesn’t consider much granular
detail and doesn’t consider many “facets” of the problem.
For example, a model of this nature might work by
projecting forward the capacity requirements of some
typical substations under multiple future scenarios, and
different intervention strategies.
ThesemodelsareplottedinFigure1intermsofnumberof
dimensions and granularity.
In GB, two pieces of pioneering analysis of the GB
distribution network undertaken by the industry wide
Smart Grid Forum are instructive for illustrating some of
the trade-offs of different approaches.
Figure 1 : Summary of distribution network techno-economic
modelling approaches

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“whole system” issues: for example, during summer when
demand is low and PV output is high, high generation
output on an 11kV network can cause overvoltages on the
LV system, due to the lack of automatic voltage control,
as the primary voltage of the 11kV/400V transformer
changes.Thiswouldn’tbeobservedinatypicalmodelthat
considers every voltage level in isolation.
scenario and representative network models can then be
undertaken by combining several approaches:
Network EmulatorModel
In this novel approach, an ‘emulator’ network model is
The thermal and voltage response of the network to a
wide range of input loads is modelled (based on 1000’s
of different snapshots of demand and generation loads).
Taking a subset (typically 80%) of this data, an equation
is fitted for each node and branch that regresses the
outputs against the inputs.This can be for all nodes or pre-
selected nodes likely to be most heavily loaded or at risk
of voltage issues such as cable incomers to transformers
and ends of feeder. This reduces computational run time
with fewer nodes. The remaining 20% of the data is used
to validate the regression. It should be noted that voltage
interactions can be more complex however, multivariate
and non-linear regressions can be used to describe these.
Equally, the approach can be applied to both radial and
meshed networks. Simple linear regression is illustrated
here however, an emulator approach could be based on
Gaussian processes, neural networks etc as needed.
Transparency: One other challenge (which is not strictly
a modelling challenge) is that there is often an underlying
requirement for models of this nature to be transparent
for all stakeholders. Techno-economic models of the
distribution networks inform many important strategic
and policy decisions, and many informed and engaged
stakeholders may have an interest in both the outputs of
these models and how these outputs were produced. This
might constrain the level of sophistication which can
be used within such models, which might in turn limit
their ability to account for some of the complex factors
described above. For example, there is often a constraint
that models like this are built and operated using Excel so
that any stakeholders can pick them up and explore them.
3. Advanced modelling
techniques
We have explored a number of advanced modelling
techniques that enable an increase in both the number of
the dimensions that can be considered and the granularity,
without adding significantly to computational time.
These are described below in the context of a distribution
investment model methodology. We have developed and
tested this methodology on a number of representative GB
distribution networks.
Forstrategicnetworkplanning,anumberofrepresentative
network models (e.g. urban, rural) can be built in a power
flow modelling software as shown in Figure 2. These can
be modelled as a ‘slice’of the network from EHVdown to
theLVtocaptureholisticvoltagebehaviourforexample.At
LV,themodelsshouldenablethevarianceofimpactdueto

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Modelling ofVariability Between Customers
series data for higher voltage networks, based on historic
assumptions about the demand per customer, e.g. After
a single value of per customer demand for one or more
customer types, which are consistent across the whole
network. Even though the variability demand between
different customers (even of the same type) is significant
enough that they are almost certain to provide sufficient
capacity for all customers. There is a risk that this leads to
over- engineerednetworks, butas DNOs tend toonlyhave
a limited number of discrete asset sizes to choose from,
this risk has historically been reasonably low.
While these simple assumptions may have been
appropriate for sizing traditional assets, they will not
be appropriate when trying to determine the remaining
capacity on a network (e.g. the capacity for connecting
EVs and heat pumps) or when assessing the feasibility
of some flexibility measures. However, these sorts of
assumptions are frequently used in strategic distribution
networkmodelling,treatingallcustomersofthesametype
as if they have exactly the same patterns of demand. This
could have a significant impact on the outputs of these
sorts of models, particularly where significant new loads
are being added.This is likely to mask the “skew” of costs
towards more highly loaded networks as well as affect the
This approach has been tested successfully for a number
of networks from LV to EHV and for thermal loading and
single variable (total downstream demand) are shown in
coefficient of determination ranged from 0.74 to 1, with
lower values indicating that more sophisticated regression
equations should be used.
For HV and EHV networks, modelling complexity
increases as parametric equations need to be derived for
various security of supply conditions (likely network
outages). However, appropriate selection of key nodes
can keep computational time to a reasonable level. Also,
the network emulator can be a background computational
process that refreshes to reflect any network topology
changes. It does not need to be rerun at each time of use.
This novel approach can allow a wide range of network
input conditions to be assessed rapidly, as a power flow
analysisdoesnotneedtobererunforeveryconditiononce
the network emulator model is available. This can help to
speed up strategic modelling run-times. If assessing the
network probabilistically, this approach has significant
be run in order to produce robust results. This approach
could be applied to increase the volumes of lower voltage
distribution networks that are analysed in a more detailed
way.

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Modelling Interventions
In network planning, once a thermal constraint or
voltage issue has been identified, a network intervention
is optioneered and deployed. The network planner’s
decision-making process is driven by a number of factors
(some subjective) but at a high level, the intervention is
selected on the basis of the capacity it provides and its
lifetime cost. This can be modelled within a reasonably
simple cost minimisation problem.
In our approach, a cost function is used, similar to other
including disruption costs, cross-network benefits, life
expectancy, and the flexibility of the solution, as well as
capex and opex. It also includes an estimate of associated
enabler costs, wider scale changes such as monitoring or
controlthatwouldberequiredforanewsolution.Therefore,
the cost function accounts for many of the wider factors a
DNO would consider when making an investment in their
networkratherthanjustthetotalcostofthesolution.These
factorsarerepresentedintermsof‘costs’intheoptimisation
model, to allow for their consideration, however only the
actual capex and opex are used when appraising the cost
to the DNO. These estimated costs are then used within a
constrained optimisation algorithm, to find the lowest cost
combination of solutions that can address the constraints
that have arisen on the network.
This approach can be used to represent market-based
accounting for the influence of price elasticity can become
complex and, in some cases, may be better captured
through load inputs rather than ‘interventions’.
4. Conclusions
In this paper, we have set out some advanced modelling
techniques that can be used to expand both the granularity
of strategic distribution network modelling as well as the
number of “dimensions” that such models can consider.
However, as Table 1 suggests, the number of factors
that might need to be considered in these models is very
extensive. Also, as this paper highlights, the inclusion
types of interventions that are deployed within the model,
in particular by underestimating the role for flexibility
solutions.
Whilst existing methods provide good foundations for LV
network planning, novel analysis techniques can evolve
these so that new sources of data can be leveraged to
address emerging challenges. We have developed a novel
analysis technique to represent customer load that uses a
sophisticatedstatisticalmodeltoreflectboththevariability
and uncertainty in demand on LVnetworks.This draws on
a branch of statistics known as Bayesian statistics.
In Bayesian statistics, probabilities are viewed as
representing subjective beliefs, rather than the long-
run frequency of some measured phenomenon. This is
important when dealing with problems for which there is
notmuchdata.Initialbeliefsareformalisedmathematically
as ‘prior probability distributions’. Our method proposes
that prior probability distributions should be formed based
on existing smart meter data sets from innovation projects
suchasthosegatheredintheUKthroughconsumerfunded
innovation projects.
Bayesian statistics allows for the initial prior beliefs
to reflect the uncertainty which exists when trying to
understand the demand for customers at the LV level,
without specific local data. When data becomes available
fromsmartmetersorLVmonitoring,itcanbeusedtoupdate
the ‘prior’, according to a procedure known as Bayesian
updating, to form a ‘posterior probability distribution’.
It is expected that this will reduce the uncertainty in the
estimate.Thisprocedurecanberepeatedindefinitelyevery
time new data becomes available. Eventually, the initial
prior belief will have very little influence on the modelled
demand.
This probabilistic approach to representing loads has been
coupled with the network emulator model to produce risk-
theutilisationdistributionsofanumberofruralLVfeeders
for a one-in-ten year utilisation. It can be seen that feeder
2 is expected to exceed a 100% utilisation at least once in
every ten years.

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a strategic model until it was also incorporated within the
day-to-day decision-making processes of the distribution
company1.
5. Bibliography
[1] UK Power Networks. Impact of Electric Vehicles and Heat Pump
Loads on Network Demand Profiles. Low Carbon London.
September 2014. https://innovation.ukpowernetworks.co.uk/wp-
content/uploads/2019/05/B2-Impact-of- Electric-Vehicles-and-
Heat-Pump-Loads-on-Network-Demand-Profiles.pdf
[2] Northern Powergrid. Insight Report Electric Vehicles. Customer
Led Network Revolution. December 2014.
[3] Scottish and Southern Electricity Networks, EA Technology. My
Electric Avenue. September 2016. http://myelectricavenue.info/sites/
default/files/Summary%20report.pdf
[4] Muratori, Matteo (2017): Impact of uncoordinated plug-in electric
vehicle charging on residential power demand - supplementary
data. National Renewable Energy Laboratory. https://dx.doi.
org/10.7799/1363870.
[5] European Commission. Identification of Appropriate Generation
and System Adequacy Standards for the Internal Electricity
Market. March 2016.
[6] National Grid ESO. Electricity Ten Year Statement, November 2018.
Great Britain.
[7] Smart Grid Forum, Workstream 3 Assessing the impact of Low
Carbon Technologies on Great Britain’s Power Distribution
Networks. July 2012.
[8] Smart Grid Forum, Workstream 7 DS2030. December 2015.
[9] European Commission. MERGE (Mobile Energy Resources in Grids
of Electricity). December 2011. https://cordis.europa.eu/project/
rcn/94380/reporting/en
[10] Northern Powergrid and TNEI Services Limited. Smarter Network
Design Methodologies. Novel Analysis Techniques at Low Voltage.
June 2019.
______________________
1 - However, this also suggests that such a model could be used to determine the
benefits of employing a risk- based approach.
of these factors typically needs to be balanced against
an overarching requirement for transparency, so that all
stakeholders can understand the operation and output of
the models.
As a result, there are some fundamental limits to what
can practically be achieved in a single techno- economic
no model can account for all of these factors while still
being accessible to those that weren’t initially involved in
developing it. Therefore, it is important that analysts are
clearaboutwhatquestionstheyaretryingtousethemodel
to answer, in order to focus the analysis on those factors
that are most material to the particular question. This
means that analysis and stakeholders need to balance the
materiality of different factors alongside the tractability of
including them in the model.
One important question that might help focus the scope
of such a model is whether the model needs to be capable
of determining the absolute cost of some future scenarios
or just to compare the relative costs between scenarios.
The former might be required, for example, when
agreeing a price control between the regulator and the
distribution network company. In these situations, it will
be important for the network company to understand the
range of possible costs as well as the variables which drive
this, so that this uncertainty can be accounted for in the
structure of the price control. On the other hand, models
whichcalculatetherelativecostbetweenscenariosmaybe
sufficientiftryingtomakebroaderpolicydecisions,where
each policy might lead to a different scenario outcome. In
these cases, it may be justifiable to simplify or abstract
some aspects of the model. However, in these cases, care
should be taken when making comparisons between the
outputs of this model with any other metrics.
Finally,itisimportantthatsuchmodelsrepresentthereality
of the actual network planning and investment decision
making processes they are representing. This might also
limit the ability to make models more sophisticated, as
the models should not be more sophisticated than the real
processes. For example, the risk-based customer demand
model approach described in this behaviour would require
a clear articulation of how these risks are accounted for
in network planning by, for example, defining the level of
risk to which networks are designed. Therefore, it would
be difficult to incorporate such a risk-based approach into