Planning of distribution networks considering flexibility of local resources: how to deal with transmission system services

Abstract

Modern planning techniques for distribution systems consider, in addition to the conventional grid reinforcement, the provision of power flexibility from local resources. This solution is demonstrated to be cost-effective in numerous cases. However, distribution resources might be required to provide services to transmission system too, and this aspect needs to be considered within the selection of the best distribution planning options. This paper investigates a distribution network planning strategy based on different trade-offs between "minimization of investment costs" and "maximization of distribution flexibility for transmission services", which is aimed at supporting a cooperative (but decoupled) planning for both distribution and transmission systems.

Full text

CIRED 2021 Conference Geneva, 20 – 23 September 2021
Paper 976
1
Planning of distribution networks
considering flexibility of local resources:
how to deal with transmission system services
Marco Rossi1*, Matteo Rossini1, Giacomo Viganò1, Gianluigi Migliavacca1,
Dario Siface1, Izabella Faifer1, Hakan Hergun2, Iver Bakken Sperstad3
1Ricerca sul Sistema Energetico - RSE SpA, Milan, Italy
2KU Leuven, Leuven, Belgium
3SINTEF Energy Research – SINTEF Energy Research, Trondheim, Norway
*marco.rossi@rse-web.it
Keywords: DISTRIBUTION NETWORK PLANNING, LOCAL FLEXIBILITY, STORAGE,
TRANSMISSION NETWORK PLANNING, ANCILLARY SERVICES
Abstract
Modern planning techniques for distribution systems consider, in addition to the conventional grid reinforcement, the
provision of power flexibility from local resources. This solution is demonstrated to be cost-effective in numerous cases.
However, distribution resources might be required to provide services to transmission system too, and this aspect needs to be
considered within the selection of the best distribution planning options. This paper investigates a distribution network
planning strategy based on different trade-offs between “minimization of investment costs” and “maximization of distribution
flexibility for transmission services”, which is aimed at supporting a cooperative (but decoupled) planning for both
distribution and transmission systems.
1. Introduction
The evolution of power systems introduces new challenges in
terms of operation and planning, and current research is
demonstrating how local services (provided by flexible
demand/generation/storage) can compete with the
conventional network reinforcement at any voltage level. In
fact, literature [1]-[8] proposes many distribution network
planning strategies aimed at determining the best trade-off
between local flexibility and new lines/transformers. All of
them are clearly showing how the current practices (based on
manual procedures and worst-case scenarios analysis) are not
leading to optimal solutions. On the contrary, the adoption of
dedicated optimization techniques (based on time series
processing and multiple scenario analysis [1][2]) assists the
selection of the most cost-effective planning option for the
solution of local problems.
Nevertheless, distribution network resources have the
potential of providing services to the transmission system too
and this is a standard requirement already (especially in terms
of curtailment of renewable generation [3]). This means that
distribution system operators might be required to
operate/plan their network in order to guarantee a given
amount of local flexibility for transmission services [4]-[6].
However, there is an evident and unexplored conflict
between:
• the minimization of the costs related to local congestion
management and
• the maximization of the distribution flexibility that can
be exploited for transmission services.
For this reason, numerous research initiatives [9][10] and
working groups [1][3][5] are suggesting enhanced
cooperation among transmission and distribution operators,
having the objective of minimizing the operational and
planning costs for the entire system [7].
The absolute optimal planning solution, which considers the
necessities of the system at any voltage level simultaneously,
can be achieved by merging the models of transmission and
distribution systems in a single, joint optimization problem.
However, in addition to requiring a significant (and probably
unbearable) computational burden [2][8], the lack of
transparency and standards to exchange information among
system operators is one of the barriers for a joint planning of
transmission and distribution networks [1].
Having considered the challenges discussed above, this paper
proposes a procedure for distribution network planning
which, in addition to the solution of local issues, guarantees:
• the consideration of potential transmission system
requirements in terms of ancillary services, by
trading-off investments cost and exploitable flexibility;
• facilitated negotiation between transmission and
distribution operators for the selection of planning
options which meet the requirements of both the
systems, with no exchange of detailed network
information.
2. Case study and network model
In order to illustrate the planning strategy, the European
configuration for the CIGRE Medium Voltage distribution
network benchmark [11] has been adopted as reference. In

CIRED 2021 Conference Geneva, 20 – 23 September 2021
Paper 976
2
particular, all the switches have been considered to be
operated in their normal state and the capacity of the grid
lines are selected to be 6.5 MVA for underground cables and
5.5 MVA for overhead lines.
2.1. Modifications of the standard benchmark case
The case study focuses on the solution of the planning
problem, having considered the availability of local storage
units for the provision of congestion management services.
For this reason, all the remaining loads and generators are
assumed to be non-dispatchable and to feature the 1-day
power profiles suggested by [11].
The considered distribution network, in its current
configuration, is not subject to any congestion risk. For this
reason, the planning problem is studied having supposed an
increased wind generation (connected to bus 7), from the
existing 1.5 MW to 9 MW. Finally, an additional storage unit
is assumed to be connected to bus 14, such that the entire
system features:
• a 0.6 MW – 1.2 MWh storage unit on bus 5;
• a 0.2 MW – 0.4 MWh storage unit on bus 10;
• a 1.0 MW – 2.0 MWh storage unit on bus 14.
All these units are assumed to have a 90% efficiency during
both the charging and discharging phases, and a 0.1% hourly
self-discharge rate.
2.2. Planning candidates
The operation with 9 MW wind power is not feasible within
the considered system: some lines would be significantly
overloaded and voltage problems may occur if not properly
managed (reactive power flexibility of generators and tap
changing transformers are assumed to be adjustable at no
operational cost). In order to face the possible congestions,
the following planning candidates have been foreseen:
• Each existing branch can be reinforced by an alternative
line/transformer characterized by twice its conductance
and capacity (this is equivalent to the installation of two
branches in parallel). The cost for line reinforcement is
assumed to be equal to:
o 150 k€/km for underground cables;
o 60 k€/km for overhead lines;
o 350 k€ for (50 MVA) transformers.
• Each existing storage can be doubled in rated power and
capacity, having considered investment costs equal to
350 k€/MWh.
In addition to these candidates, flexibility of existing storage
is also considered by the planning procedure. According to
that, their related operational costs are included within the
objective function and depend on the wholesale energy price,
which is assumed to be equal to 50 €/MWh over the entire
planning horizon.
The case study is limited to few simple candidates, aimed at
returning intuitive and easy to be interpreted results. In fact,
the paper focuses on the proof-of-concept for the proposed
methodology, which can be anyway applied to more complex
situations and sets of possible investments.
2.3. Model of upgradable distribution network
The planning of a distribution network consists of the
selection of the grid expansion options (new lines and
transformers) and flexible units (dispatchable storage) which
guarantee the minimum capital and operational expenditures
for the solution of existing or potential issues. Therefore, it
can be formulated as a classical optimization problem and,
since binary investment decision variables needs to be
managed, a (mixed-integer) linear formulation is the
preferred option [2], especially for large power systems
which can be also characterized by a significant amount of
expansion candidates.
Given that voltage is one of the electrical quantities to keep
under control for distribution network operation and planning
[12], a linear approximation of the AC Optimal Power Flow
(OPF) is adopted [13]. Thanks to this formulation, which is
recognized to be accurate for conventional Medium Voltage
distribution grids, a computational efficient algorithm can be
easily coded and processed for the planning options described
in the following section.
3. The proposed planning strategy
As anticipated above, there is conflict between the
minimization of the planning costs (limited to distribution
network needs) and the delivery of power flexibility to the
transmission network. For this reason, the proposed strategy
is based on the iterative exploration of a number of possible
planning options which cover different trade-offs in terms of
costs for local network investments and available capacity for
transmission services. Each option consists of the result of
more optimization problems, which can be sequenced in
different ways. The proposed strategy can be summarized
with the three main steps described in the following
subsections.
3.1. Step 1 – Minimization of the planning costs for
distribution network
The most intuitive and common procedure for the planning of
distribution system consists of minimizing the operational
and investment costs for the solution of local network issues,
regardless of transmission potential needs. For the considered
case study, the objective function is composed by two terms:
• Storage operation cost, which results from internal
losses and energy price. The resulting cost is re-scaled
to consider a 10-year lifespan (having assumed the
investigated 24-hour time slot to be recurrent).
• Line/transformer/storage investments, which actual
costs are reported in section 2.2.
By processing the optimization problem for the considered
case study, the returned solution can be summarized as:
• upgrade of the lines connecting buses 3-8 and 7-8;
• operation of existing storage units connected to bus 5
and bus 10, for congestion management services;
• total operational + investment costs of about
245 + 446 k€.

CIRED 2021 Conference Geneva, 20 – 23 September 2021
Paper 976
3
In fact, looking at the results reported in Figure 1, the loading
of the substituted lines is exceeding the initial 6.5 MVA
capacity during the wind production peaks.
Figure 1. Loading of lines originally subject to congestion
risk when step 1 candidates are selected.
Optimization results clearly indicate that also storage units
are contributing to the reduction of network issues. Looking
at their power exchange profiles (Figure 2), the existing units
connected to bus 5 and bus 10 are absorbing active power in
order to prevent the congestion of the lines connecting
bus 1-bus 2 and bus 2-bus 3 (Figure 1).
Figure 2. Active power exchanged by existing storage units
when step 1 candidates are selected.
Once the optimal investments have been defined, the portion
of local flexibility remaining from the local congestion
management can be used for services supporting the
operation and planning of the upstream transmission network.
This portion can be quantified by fixing the selected
investments and running two separated OPF routines with the
following objective functions:
• maximization of the active power import from
transmission to distribution system;
• maximization of the active power export from
distribution to transmission system.
Although the implementation of this optimization problem
seems to be intuitively correct, the solution of the proposed
OPF with these objective functions can be misleading. In
fact, the constraints in terms of storable energy limit the
maximum power that can be delivered by the storage units in
a given time period. Since storage flexibility can be requested
anytime for transmission services, the removal of
inter-temporal constraints (i.e. energy accumulation) is
meaningful for this optimization step. This means that
storage units are temporarily modelled as dispatchable
devices, featuring the same power capability.
Figure 3 reports the result of these two optimization routines,
which makes evident the availability of a power flexibility
bandwidth around the baseline profile resulting from the
cost-minimization problem.
In order to evaluate, for each storage unit, the portion of
available flexibility that can be exploited for transmission
services, the power profiles obtained from these three
optimization procedures are compared (Figure 4).
Figure 3. Possible active power exchange profiles between
distribution and transmission system when step 1 candidates
are selected.
Figure 4. Storage units active power profiles returned by the
step 1 of the proposed planning procedure.
From their analysis, it can be clearly recognized that:
• Storage units connected to bus 5 and bus 10 are
involved in local services and cannot be considered
constantly available for other purposes. In fact,
maximum import/export profiles overlap the baseline in
case of local congestion.
• Storage unit connected to bus 14, instead, can be
exploited in its full power capability, since the obtained
profiles result to be flat, i.e. not affected by distribution
services and bottlenecks.
In conclusion, having considered that local congestion
management is not generally correlated to reserve needs at

CIRED 2021 Conference Geneva, 20 – 23 September 2021
Paper 976
4
transmission level, the unit connected to bus 14 represents the
only reliable resource capable of providing flexibility
services to the upstream network. Therefore, in case this
planning option is selected, the transmission system would
see a single equivalent storage unit, featuring 1.0 MW active
power capability and 2.0 MWh storable energy.
3.2. Step 2 – Maximization of distribution network
energy export/import
Another interesting planning option consists of identifying
the network investments capable of maximizing the
exploitability of local flexible resources for transmission
services. The solution to this problem is obtained by using a
similar procedure but reversed with respect to the previous
step. In this case, the maximum power export/import profile
is defined before the selection of the planning investments,
and it is obtained by running (again) two separated OPF
routines with the following objective functions:
• maximization of the active power import from
transmission to distribution system;
• maximization of the active power export from
distribution to transmission system.
Contrarily to step 1, planning candidates are freely selectable
within this optimization problem, guaranteeing the maximum
power flexibility transfer to the upstream network (at any
cost) achieved. Figure 5 demonstrates that a significantly
larger regulation bandwidth can be achieved around the
baseline profile (which is calculated later in the process).
Figure 5. Possible active power exchange profiles between
distribution and transmission system when step 2 candidates
are selected.
According to the adopted objective functions, investment
costs are not influencing the selection of the available
candidates. For this reason, some of them can be built even if
they are not contributing to the achievement of the maximum
power export/import. Therefore, a further OPF problem is
solved by minimizing the investment costs, having imposed a
power exchange with the transmission network equal to the
maximum export/import profile. Thanks to this strategy,
unnecessary investments are removed, and for the considered
case study the results are:
• upgrade of the lines connecting buses 1-2, 2-3, 3-8, 7-8;
• power/energy upgrade of all existing storage units;
• investment costs equal to 2,792 k€.
Until now, step 2 disregards the inter-temporal constraints for
the same reasons described in the previous subsection.
However, in order to investigate the actual exploitability of
storage units for transmission/distribution services, their
physical behaviour needs to be fully modelled (including
energy capacity limitations, conversion efficiency, etc.).
Therefore, having enabled the inter-temporal constraints and
fixed the network investments to the ones listed above, a new
OPF is carried out with the objective of minimizing the costs
related to storage operation. This last run returns the baseline
power profile for all the resources when step 2 candidates are
selected.
As expected, the increment of investments reduces the
necessity of local congestion management, leading to a
different absorption/injection of active power for the
upgraded storage units (Figure 6).
Figure 6. Storage units active power profiles returned by the
step 2 of the proposed planning procedure.
The absence of temporary limitations demonstrates the full
exploitability of the considered units for transmission
network services. This means that, in case this planning
option is selected, the transmission system would see a single
equivalent storage unit, featuring 3.6 MW active power
capability and 7.2 MWh storable energy.
3.3. Step 3 – Trade-off between minimum costs and
maximum flexibility transfer from distribution to
transmission network
Step 1 and step 2 represent two extreme cases in terms of
distribution network planning, which lead to significantly
different investment decisions and costs. Having considered
the number of candidates, it is reasonable to assume that
intermediate planning options are existing and their inclusion
within the set of choices can increase their granularity (which
is beneficial for the optimality of the final planning decision).
Reasonable planning option could clearly involve
investments which are more expensive than the ones returned
by step 1, while cheaper than the ones of step 2. According to
that, having selected an arbitrary budget for investments
within this range of costs, the exact procedure proposed for
step 2 is repeated (having added the budget constraint to the
considered OPF problems).
For the case study, just one intermediate option (with
investments budget set to 1,619 k€) has been processed and
the selected investments consist of:
• upgrade of the line connecting buses 3-8 and 7-8;
• power/energy upgrade of the storage units connected to
bus 5 and bus 14.

CIRED 2021 Conference Geneva, 20 – 23 September 2021
Paper 976
5
The evaluation of the amount of storage flexibility
exploitable for transmission services can be computed as
performed in step 2. In this case, the upgraded capability of
the unit connected to bus 14 is fully exploitable. However, it
is interesting to notice that the upgrade of the unit connected
to bus 5 is not providing apparent benefits. This can be
explained by observing that bus 5 is located in a congested
area of the distribution network, and the local storage unit can
be freely exploited for a limited amount of time. Therefore,
this planning option would be seen by the transmission
system as an equivalent storage unit, featuring 2.0 MW active
power capability and 4.0 MWh storable energy.
Table 1 summarizes the main outcome of the entire
distribution planning procedure and highlights the amount of
equivalent storage flexibility that can be exploited for
transmission services in the considered alternative cases. Of
course, the higher the investments budget, the greater is the
volume of the exploitable services.
Table 1. Overview of the planning options resulting from the
analysis of the case study.
Planning option
Investment
costs [k€]
Storage flexibility for
transmission services
step 1
446
1.0
MW / 2.0
MWh
step 3
1
,
566
2.0
MW / 4.0
MWh
step 2
2
,
792
3.6
MW /
7.2
MWh
4. Conclusion
This work proposes a planning strategy for distribution
networks, capable of exploring several planning options in
terms of minimization of the operational/investment costs and
maximization of local flexibility for the provision of services
to the upstream transmission network. Although the
procedure is characterized by a non-negligible complexity, its
adoption introduces significant advantages for a global
optimization of distribution and transmission systems:
• Automatic and independent distribution planning
routine, which explores different options in terms of
required regulation reserve for transmission services.
• Cooperation between system operators is expected to be
simple and efficient, since the identified distribution
planning options can be negotiated with a limited
exchange of standard and non-sensitive information.
This approach, which is in its early development phases, has
the main goal of supporting the decoupling of planning
routines for transmission and distribution networks. This
increases the computational tractability compared to solving a
fully coupled joint optimization problem while still
considering the interactions among different voltage levels.
5. Acknowledgements
The research leading to this publication received funding
from the European Union’s Horizon 2020 research and
innovation programme under grant agreement no. 863819.
6. References
[1] Silvestro, F., Pilo, F., Araneda, J.C., et al. ‘Review of
Transmission and Distribution Investment Decision
Making Processes under increasing Energy Scenario
Uncertainty’. 25th Int. Conf. and Exhibition on
Electricity Distribution – CIRED, 2019
[2] Schaefer, F., Menke, J., Marten, F., Braun, M., ‘Time
Series Based Power System Planning Including Storage
Systems and Curtailment Strategies’. 25th Int. Conf. and
Exhibition on Electricity Distribution – CIRED, 2019
[3] Joint Working Group CIGRE/CIRED C1.29, ‘Planning
Criteria for Future Transmission Networks in the
Presence of a Greater Variability of Power Exchange
with Distribution Systems’. 2017
[4] Pilo, F., Mauri, G., Bak-Jensen, B., Kampf, E., Taylor,
J., Silvestro, F., ‘Control and automation functions at
the TSO and DSO interface – impact on network
planning’. 24th Int. Conf. and Exhibition on Electricity
Distribution – CIRED, 2017
[5] Pilo, F., Mauri, G., Hanlon, S., et al., ‘Control and
automation systems for electricity distribution networks
of the future – an update on the activities of joint
working group CIGRE C6.25/ B5 /CIRED’. 23rd Int.
Conf. and Exhibition on Electricity Distribution –
CIRED, 2015
[6] Kornrumpf, T., Zdrallek, M., Roch, M., et al.,
‘Flexibility Options for Medium Voltage Grid
Planning’. 24th Int. Conf. and Exhibition on Electricity
Distribution – CIRED, 2017
[7] EPRI, ‘Developing a Framework for Integrated Energy
Network Planning (IEN-P)’. California, 2018.
[8] Jendernalik, L., Giavarra, D., Engels, C., et al., ‘A
Holistic Network Planning Approach: Enhancement of
the Grid Expansion using the Flexibility of Network
participants’. 24th Int. Conf. and Exhibition on
Electricity Distribution – CIRED, 2017
[9] Migliavacca, G., Rossi, M., Siface, D., et al., ‘The
Innovative FlexPlan Grid-Planning Methodology: How
Storage and Flexible Resources Could Help in De-
Bottlenecking the European System’. Energies 2021,
14(4), 1194
[10] Rossi, M., Migliavacca, G., Viganò, G., et al., ‘TSO-
DSO coordination to acquire services from distribution
grids: Simulations, cost-benefit analysis and regulatory
conclusions from the SmartNet project’. Electric Power
Systems Research, Proceedings of the 21st Power
Systems Computation Conference (PSCC 2020)
[11] CIGRE Task Force C6.04,‘Benchmark Systems for
Network Integration of Renewable and Distributed
Energy Resources’. 2014
[12] Aigner, C., Witzmann, R., ‘Effectivity of Active
Voltage Control Concepts in Distribution Grids’. 25th
Int. Conf. and Exhibition on Electricity Distribution –
CIRED, 2019
[13] H2020 EU project FlexPlan, ‘Probabilistic optimization
of T&D systems planning with high grid flexibility and
its scalability’. 2021