# Renewable energy communities: do they have a business case in Flanders?

Alex Felice  Lucija Rakocevic  Leen Peeters  Maarten Messagie  Thierry
Coosemans  Luis Ramirez Camargo

###### Abstract

Renewable energy communities (RECs) are prominent initiatives to provide end
consumers an active role in the energy sector, raise awareness on the
importance of renewable energy (RE) technologies and increase their share in
the energy system thus reducing greenhouse gas emissions. The economic
viability of RECs though, depends on multiple interdependent factors that
require careful examination for each individual context. This study aims at
investigating the impact of electricity tariffs, ratio of electrification of
heating and transportation sectors, prices of RE technologies and storage
systems, and internal electricity exchange prices on the annual cost for
electricity provision of a REC. A mixed-integer linear model is developed to
minimize energy provision costs for a representative REC in Flanders, Belgium.
The results indicate that RECs have the potential to reduce these costs by 10
to 26% compared to business-as-usual. This cost reduction depends on the type
of electricity tariffs and the level of uptake of flexible assets such as heat
pumps and electric vehicles. The shift towards a higher power component in the
electricity tariff makes electricity storage systems more attractive, which
leads to higher electricity self-consumption. The introduction of flexible
assets adds the possibility to shift demand when tariffs are lower and makes
larger sizes of photovoltaic systems economically viable due to the increase
in the total electricity demand. However, RECs cost reduction compared to
individual smart-homes amounts to only 4% - 6% in the best cases.
Uncertainties stemming from the regulation and the costs of setting up a REC
may reduce the estimated benefits.

###### keywords:

renewable energy communities , multi-energy system optimization , energy
transition , flexibility ,

††journal:

[inst1]organization=Electric Vehicle and Energy Research Group (EVERGI),
Mobility, Logistics and Automotive Technology Research Centre (MOBI),
Department of Electrical Engineering and Energy Technology, Vrije Universiteit
Brussel,city=1050 Ixelles, country=Belgium [inst2]organization=Th!nk
E,addressline=Philipssite 5, city=3001 Leuven, country=Belgium

##  1 Introduction

Renewable energy communities (RECs) are indicated as one of the means to help
democratize, decarbonize and decentralize the energy sector across Europe. As
defined in the recast of the European Renewable Energy Directive (RED II) [1],
a REC is a legal entity entitled to produce, consume, store and sell renewable
energy between geographically co-located private citizens, public entities and
SMEs. Their objectives are to create economical, environmental and social
benefits to the community members, as well as to increase local acceptance of
renewable energy projects [2]. Although the RED II provides a framework for
the implementation of RECs, the specific conditions for each country depend on
the transposition at the individual EU member state level, meaning that there
are still uncertainties on the conditions for an extensive uptake of RECs [3].
Ines et al. [4] highlight the transposition problem by comparing regulations
of nine different European countries or regions. They identified that the
first challenge for RECs implementation is to overcome local legal barriers in
order to exploit the opportunities brought by the legal framework at EU level.
According to Brummer et al.[5], this dependency on regulations cuts both ways:
regulation promoting RECs may be fruitful for their uptake, but it might
present a weakness for long term development of RECs.

When the regulation allows the development of RECs, the next question is to
understand the motivation of citizens to join a REC. For instance, Conradie et
al.[6] focus on better understanding the factors that influence members’
participation in a community in Flanders, Belgium. They showed that lowering
the practical barriers of entry in a REC are not sufficient alone. Attitude
towards renewable energy sources (RES), ecological impact and expected
financial gains are also motivators. According to Bauwens et al.[7],
acceptance of new RES projects is higher for RECs’ members than for non-
members, highlighting their social impact. The importance of the economical
benefits, which will determine how much investment will be done in new
community renewable energy projects, is analyzed in another work of Bauwens et
al. [8]: results show that the return on investment is the most important
determinant for members of large communities of interest, while environmental,
social and other non-economic drivers tend to dominate financial motives for
members of smaller communities of place. This result is also in accord with
the RED II [1]: the main objective of a REC should not be a pure financial
gain. In this study, we propose a MILP optimization model where we incorporate
different non-technical factors, such as tariffs or investment’s strategies,
that could influence RECs performance. This is done by creating 156 different
scenarios and analyzing their impact on the final electricity costs for the
users, as well on uptake of PV and BESS. Aside from final annual costs, we use
self-consumption and self-sufficiency measures as indicators of performance.

##  2 Literature review

A REC can be viewed as economically viable when the total energy cost for the
community members are at parity with or lower than other options for energy
supply. Community members are assumed to be consumers or prosumers.
Electricity tariff directly affects the economic viability of a REC. Therefore
a detailed analysis of both technical and non-technical aspects of REC are
needed to understand its economic viability. Radl et al. [9] compared
photovoltaic (PV) and battery energy storage systems (BESS) profitability in
multi-energy RECs for eight different European countries. The authors
concluded that except for cases of full-load hours dictated by weather
conditions, the electricity tariffs has the highest impact on PV investments.
Concerning BESS, they concluded that under current market conditions they are
rarely profitable except when capacity based pricing is applied.

Integral part of electricity tariff is the network tariff. New network tariff
structures may also impact the REC business case. Traditionally, the main part
of consumers’ network tariffs are based on their volumetric electricity
extraction from the grid, in €/kWh. With an increasing share of prosumers, who
may both extract and inject electricity to the grid, and a growing challenge
of managing power peaks in the grid due to more intermittent generation, the
traditional volumetric network tariffs have become outdated. The affordability
of decentralized RES has led to an increasing number of consumers with an
alternative energy supply and thus an ability to react and momentarily opt out
of the energy supply from the grid. Tariffs for prosumers and energy
communities need to reflect how these types of consumers now have an
alternative energy supply but remain connected and still dependent on the
grid, and that power flows go two ways [10]. There is a consensus that
volumetric tariffs with net-metering are unfit for the future high-RES energy
system (e.g. [11]; [12]; [13]). The process of reviewing tariff structures has
therefore been initiated in many countries. Abada et al. [14] studied the
impact of electricity tariff design on energy community formation. They find
that fixed tariffs lead to REC formation while also generating the most social
welfare and avoiding over-investments. Capacity based and volumetric tariffs
incentivize RES investments, but may also lead to a welfare destructive
snowball effect of over-investments. The impact of potential future tariff
structures on the business case for REC is however not well understood. It is
important to better understand the interaction between the new tariffs and the
promotion of REC, in order to streamline the policy initiatives.

Another important point mentioned on the RED II [1] is the use of renewable
electricity production in heating and cooling and transport sectors for
reducing greenhouse gas emissions and fuel dependency. At residential level
this can be effectively achieved by increasing the usage of technologies like
heat pumps (HPs) and electric vehicles (EVs). The electrification of heating
and cooling and transportation sectors at the building and neighbourhood level
introduces the need for appropriate techno-economic models for multi-energy
systems in order to identify optimal investments and operational strategies.
However, the challenge for modelling RECs is not only technical but also
policy dependent. This increases the complexity and the computational
resources needed for such techno-economic models to be used. The literature
covering optimization of RECs, or district-level multi-energy systems in
general, is already extensive, but the inclusion of the impact of different
policy and/or investment possibilities is still under-represented. Weckesser
at al. [15] introduced the regulatory aspects by analyzing different community
configurations on the distribution grid, while optimizing the size of PV and
battery storage of a REC for minimizing costs and disturbance on the low-
voltage distribution grid. In [16] the optimal sizing and operation of energy
communities is coupled with a study of a business model for the participation
of aggregators in a REC while ensuring fair sharing of costs and revenues
between all the actors. Braeuer et al. [17] applied the German Tenant
Electricity Law, a particular regulation in place between tenants and owners
of multi-apartment buildings, to a mixed-integer linear program (MILP)
optimisation model for an energy community composed of multi-apartment
buildings. The results show how the legal framework has a direct impact the
economic viability of the REC. Another analysis regarding multi-apartment
buildings is presented in [18], where the difference in legislative framework
between Austria and Germany results in different profitability of shared PV
systems in multi-apartment buildings. Due to policy differences, the
profitability of such systems in Austria is very marginal compared to the
Germany. A policy-oriented optimization framework was developed in [19],
further highlighting the needs of merging techno-economic aspects with
regulatory ones. A previous study of the authors [20] investigates the
conditions needed by RECs to operate in an economic positive way in the
context of Flanders. Results indicate that even though user type, user
consumption and electricity tariffs are important factors, the amount of
flexible technology in a REC is the most important factor to reduce
operational costs.

The examples given above show that there is ongoing work on the coupling of
regulatory and techno-economic aspect in energy modelling, but these studies
are bound to regional and/or national levels. The novelty of this work relies
on the extensive scenarios analysis to simultaneously map the impact of most
uncertainties on the final cost for energy provision for the users and
renewable penetration by the means of RECs. To the best knowledge of the
authors this is the first study tackling these subjects for the case of
Flanders, and due to the expected role of RECs for the energy transition plan
in the EU it can contribute to inspire similar analysis for other regions or
countries.

##  3 Methods

###  3.1 REC set-up

The REC set up is the result of a participatory process between multiple
stakeholders. It is a synthetic REC composed by eleven real residential
buildings, located in Flanders, with their associated hourly electrical
consumption profiles for a whole year. These profiles are all provided by
Fluvius, the Flemish distribution system operator. Between these eleven
members, nine of them are single-family houses while the last two are
apartment buildings. All of them are connected to the low-voltage grid only.
The Distributed energy resources (DER) included in the system are PV, BESS, EV
chargers and controllable HP. PV output profiles are calculated by using a
single normalized generation profile for Flanders: 1,000 kWh of energy
produced in a year for 1 kWp installed, which is scaled with the different
capacities installed for every member. EV chargers demand profiles are based
on a fixed daily demand of 7 kWh. Two type of residential heating profiles are
simulated in TRNSYS [21] based on building type, usage and outside
temperature. With the same principle, heat pumps’ COP are simulated based on
heat demand and outside temperature. The heat demand profiles are then
translated in electricity demand of heat pumps using the COP profiles. All the
technical and economical parameters are listed in Table 1.

Table 1: Technical and economical parameters Parameter | Value
---|---
$\eta^{ch}$/$\eta^{disch}$ | 95 %
$SOC_{min}$ | 0.1
$\overline{p^{ch}}$ | 1
$\overline{p^{disch}}$ | 2
$m^{ev}$ | 3.5 kW
$m^{hp1}$ | 1 kW
$m^{hp2}$ | 2 kW
$lifetime^{PV}$ | 25 years
$lifetime^{BESS}$ | 10 years
$\lambda^{ext,im}_{t,n}$ | 28 c€/kWh or 21 c€/kWh
$\lambda^{ext,ex}_{t,n}$ | 3.5 c€/kWh
$\lambda^{p}_{n}$ | 4.17 €/kW/month
$d$ | 7.5 %

###  3.2 Scenarios construction

We create an extensive set of scenarios to evaluate different options of REC
set-up for having a positive business-case. The first set of scenarios
concerns the presence of HPs, for both heating and cooling, and EV chargers.
The penetration of these technologies is varied in order to quantify the
impact of different levels of flexible demand present in the system. The
second set of scenarios, Capex scenarios, show sensitivity of cost of energy
provisions on the investment cost of PV and BESS. The third set of scenarios
concerns the electricity tariffs, where a comparison between volumetric and
capacity tariff is made. The fourth set of scenarios aims at comparing three
different investment strategies: business-as-usual (no additional RES are
installed), individual investment and community investment. In the community
investment case an additional level of sensitivity analysis is performed on
the internal energy exchange prices.

####  3.2.1 Technologies scenarios

While PV and BESS are variables of the optimal planning problem for all the
scenarios, HPs and EV chargers capacities are incorporated as fixed parameters
for selected scenarios. This allows us to compare the economic viability of
RECs when technologies that provide the services of heating and transportation
using electricity, and therefore provide demand-side management (DSM)
capabilities, are available or not. Installed capacities of heat pumps are
fixed based on the maximum daily demand that needs to be satisfied, while EV
chargers’ maximum capacities are standards present in the LV grid. In total
four technology scenarios where created to assess the impact of different
degree of flexible demand present in the system. These are summarized in Table
2.

Table 2: Technology scenarios Scenario |  | % of members
---
with HP
| % of member with
---
EV charger
| % of flexible
---
demand
1 | 100 | 100 | 41.8
2 | 100 | 0 | 25.6
3 | 0 | 100 | 26.9
4 | 0 | 0 | 0

####  3.2.2 Capex scenarios

As a techno-economic optimization model is used, economic parameters like the
investment cost for newly installed PV and BESS play a fundamental role on the
outcomes of the model. In order to analyze their impact three different prices
are proposed for each technology, starting from a higher price which reflect
actual cost of installations and ending with a lower price which is expected
in the near future, or results from use of government’s subsidies. These
parameters were defined in iterative consultation with local technology
providers, DSOs and research institutions working on RECs in the Flemish
context. The three scenarios are summarized in Table 3.

Table 3: Capex scenario Scenario |  | PV installation
---
cost
| BESS installations
---
cost
1 | 1200 €/kWp | 1000 €/kWh
2 | 1000 €/kWp | 750 €/kWh
3 | 800 €/kWp | 500 €/kWh

####  3.2.3 Tariff scenarios

Three scenarios are proposed to analyze the difference in the total annual
costs and newly installed DER. We compare a common volumetric tariff to two
different capacity tariffs. The reference tariff used for this study is a
volumetric one (€/kWh) with peak and off-peak tariffs. This choice has been
made in order to exploit the DSM potential of the system, which cannot be done
if a flat tariff is used. Peak times are between 7:00 and 22:00 during
weekdays, while off-peak times are the rest of the day in weekdays and during
weekends. The two capacity-based tariffs are built by first identifying the
final cost split of an electricity tariff in Flanders. From [22], a report of
the Flemish Regulator of the Electricity and Gas Market (VREG), we have the
following structure: 28 % is the commodity part, 18 % the DSO tariff, 7 % the
TSO tariff. 17 % the VAT and 30 % are fees and taxes. The first capacity
tariff represent the planned scenario for 2022 in Flanders [22] where the DSO
tariff will be billed based on the highest monthly peak consumption (€/kW).
For the second capacity tariff all the components except VAT, fees and taxes
are kW-based, hence 53 % of the total. In all the scenarios, injection price
for over-production is fixed over the whole time-horizon. It has to be noted
that the values in Table 1 are used as a reference to build all the different
tariff and peer-to-peer scenarios explored in this study. The import prices
$\lambda^{ext,im}_{t,n}$ (average value for Flanders in 2020 [23]) represent
the purely volumetric tariff presented in Table 4, these values are then
scaled down to be used for the other tariff scenarios. The same apply for the
peak import price $\lambda^{p}_{n}$, where the value in Table 1 is the
reference value for tariff scenario 2 which is scaled up using the percentage
of tariff scenario 3.

Table 4: Tariff scenarios Scenario |  | Description
---
1 | Volumetric
2 | 18 % capacity-based
3 | 53 % capacity-based

####  3.2.4 Investment scenarios

Three investment scenarios are introduced by the mean of three different
optimization levels. The first one is the business-as-usual case, where no REC
is created and investment in PV and BESS are not introduced. This will be used
as a reference case. The second scenario is called individual investment
scenario: every member makes investment decision based on own needs, without
consideration to other members (individual objective function). In this
scenario, no REC is created, energy exchange is allowed only with the grid.
The third scenario is the community-joint investment, where the optimal
investment in PV and BESS is determined at the REC level and each member can
own a share of the assets. A REC is created, meaning that energy can be traded
inside the community and with the grid. In this last scenario costs of PV and
BESS are assumed to be 10 % lower than the individual case due to economy of
scale. The peer-to-peer exchange is possible in cases where users are not part
of the REC, but we include it only in scenario three to assure extreme cases
are analyze considering the need to limit the number of scenarios. Such
scenario design should already enable to quantify the general impact of
different strategies. Another difference between the three optimization levels
is in the usage of flexible assets: in the reference scenario, both HPs and EV
chargers demand are fixed hourly profiles in order to disable any
optimization. While in the individual investment scenario (scenario 2) and in
the REC scenario (scenario 3) these two types of profiles are transformed in
daily demand to enable hourly optimization of their usage.

Table 5: Investment scenarios Scenario |  | Description
---
1 |  | No investment and internal exchange disabled
---
2 |  | Individual investment and internal exchange disabled
---
3 |  | Collective investment and internal exchange enabled
---

####  3.2.5 Peer-to-peer scenarios

These scenarios only apply for the third investment scenario (creation of a
REC). For this particular case additional analysis are done on the impact of
the peer-to-peer energy price inside the REC. This exchange price is modeled
as the difference between the buying and selling price of energy coming from
the assets present in the community. Comparison will be made between free
internal exchange, an internal cost that correspond to 65 % (DSO cost + VAT +
taxes) and 72 % (DSO + TSO cost + VAT + taxes) of the buying price from the
grid of each tariff scenario, summarized in Table 6.

Table 6: Internal price scenarios Scenario |  | Description
---
1 | 0 % of buying price
2 | 65 % of buying price
3 | 72 % of buying price

###  3.3 Key performance indicators

In order to analyze the results, five different KPIs are used:

  * 1.

annualized total cost per kWh consumed (€/kWh):

$\dfrac{C_{inv}+C_{op}}{\sum_{\begin{subarray}{c}t\in T,\\\ n\in
N\end{subarray}}(l_{t,n}\ +\ p^{hp}_{t,n}\ +\ p^{cool}_{t,n}\ +\
p^{ev}_{t,n})\cdot\Delta t}$ (1)

where $C_{inv}$ represents the equivalent annual cost of investment, while
$C_{op}$ is the total cost for operating the energy community, both expressed
in €. $l_{t,n}$ is the power demand, $p^{hp}_{t,n}$ the heat demand,
$p^{cool}_{t,n}$ the cooling demand, $p^{ev}_{t,n}$ the EV charger demand, all
expressed in kW. $\Delta t$ the timestep size in hours.

  * 2.

PV capacity installed per MWh consumed (kWp/MWh):

$\dfrac{\sum_{n\in N}cap_{PV,n}\cdot 1000}{\sum_{\begin{subarray}{c}t\in T,\\\
n\in N\end{subarray}}(l_{t,n}\ +\ p^{hp}_{t,n}\ +\ p^{cool}_{t,n}\ +\
p^{ev}_{t,n})\cdot\Delta t}$ (2)

with $cap_{PV,n}$ being the installed PV capacity in kW

  * 3.

BESS capacity installed per MWh consumed (kWp/MWh):

$\dfrac{\sum_{n\in N}cap_{batt,n}\cdot 1000}{\sum_{\begin{subarray}{c}t\in
T,\\\ n\in N\end{subarray}}(l_{t,n}\ +\ p^{hp}_{t,n}\ +\ p^{cool}_{t,n}\ +\
p^{ev}_{t,n})\cdot\Delta t}$ (3)

with $cap_{batt,n}$ being the installed BESS capacity in kWh

  * 4.

self-consumption ratio (%):

$100\cdot\dfrac{\sum_{\begin{subarray}{c}t\in T,\\\ n\in
N\end{subarray}}p^{pv}_{t,n}-P^{ex}_{t,n}}{\sum_{\begin{subarray}{c}t\in T,\\\
n\in N\end{subarray}}p^{pv}_{t,n}}$ (4)

where $p^{pv}_{t,n}$ is the power produced by the PV and $P^{ex}_{t,n}$ the
power exported to the grid

  * 5.

self-sufficiency ratio (%):

$100\cdot\dfrac{\sum_{\begin{subarray}{c}t\in T,\\\ n\in
N\end{subarray}}p^{pv}_{t,n}-P^{ex}_{t,n}}{\sum_{\begin{subarray}{c}t\in T,\\\
n\in N\end{subarray}}(l_{t,n}\ +\ p^{hp}_{t,n}\ +\ p^{cool}_{t,n}\ +\
p^{ev}_{t,n})}$ (5)

The first three KPIs, including annualized total cost per kWh consumed, PV
capacity installed per MWh consumed and BESS capacity installed per MWh
consumed, allow the comparison between scenarios that do not have the same
total electrical consumption. Self-consumption ratio represents the ratio of
energy produced from PV that is used inside the system and not sold. It can be
seen as an indicator of over-sizing of PV. Self-sufficiency ratio is instead
the ratio between the energy locally produced and consumed and the total
energy demand, it is a measure of independence from the main grid.

###  3.4 Optimization-problem formulation

In this section, the optimization problem for finding the minimum annualized
cost for energy provision for each REC configuration is presented. The
mathematical formulation presented here refers to the third investment
scenario (see Section 3.2), the community case. In the cases of the first and
second investment scenarios part of the equations become simply zero due to
the lack of investments or no peer-to-peer exchange. The objective function
for the whole time-horizon is

$min\ C_{inv}+C_{op}$ (6)

where $C_{inv}$ is the equivalent annual cost of investment, while $C_{op}$ is
the total cost for operating the energy community. The equivalent annual cost
of investment can be calculated as

$C_{inv}=\sum_{\begin{subarray}{c}i\in I,\\\ n\in N\end{subarray}}y_{i,n}\cdot
cap_{i,n}\cdot C_{i}\cdot CRF_{i}$ (7)

with $I$ being the set of all technologies included, $N$ is the set of all REC
members. $y_{i,n}$ is a binary variable indicating if the installed capacity
of technology $i$ of member $n$, $cap_{i,n}$, is newly installed or was
already part of the system. Furthermore, $C_{i}$ is the cost for the
installation of technology $i$ and $CRF_{i}$ is the calculated capital
recovery factor for technology $i$, which is defined by

$CRF_{i}=\dfrac{d(1+d)^{lifetime_{i}}}{(1+d)^{lifetime_{i}}-1}$ (8)

where $d$ represents the discount rate and $lifetime_{i}$ is the lifetime of
technology $i$. The operational cost $C_{op}$ introduced in Eq. 6 is
calculated as

$\begin{split}C_{op}=\sum_{\begin{subarray}{c}n\in N,\\\ t\in
T\end{subarray}}\ (P^{im}_{t,n}\cdot\lambda_{t,n}^{ext,im}\ -\
P^{ex}_{t,n}\cdot\lambda_{t,n}^{ext,ex})\cdot\Delta t\ \\\ +\
\sum_{\begin{subarray}{c}n\in N,\\\ m\in M\end{subarray}}\
(P^{p}_{m,n}\cdot\lambda^{p}_{n})\ +\ \sum_{n\in N}\ C^{int}_{n}\end{split}$
(9)

which is composed by three summation terms. The first one represents the
difference between the cost of importing energy and the gain for injecting
energy back to the grid. $P^{im}_{t,n}$ and $P^{ex}_{t,n}$ are the power
imported from and exported to the main grid for every timestep $t$ and member
$n$, while $\lambda^{ext,im}_{t,n}$ and $\lambda^{ext,ex}_{t,n}$ are the
tariffs for importing an exporting energy respectively. In order to link power
values with cost per energy, we introduce $\Delta t$ as the difference between
two timesteps in hours. The second term is the sum of peak consumption cost
for every month of the year, with $P^{p}_{m,n}$ being the peak power imported
from the grid for month $m$ and member $n$ and $\lambda^{p}_{n}$ is its
associated cost. Finally, the last summation term is the the peer-to-peer
exchange costs $C^{int}_{n}$ for each member. The peer-to-peer exchange cost
for each participant is calculated in a similar way as for the energy exchange
between the community and the grid:

$C^{int}_{n}\ =\ \sum_{t\in T}\
(Q^{im}_{t,n}\cdot\lambda_{t,n}^{int,im}-Q^{ex}_{t,n}\cdot\lambda_{t,n}^{int,ex})\cdot\Delta
t$ (10)

where internal power flows ($Q^{im}_{t,n}$, $Q^{ex}_{t,n}$) and tariffs
($\lambda_{t,n}^{int,im}$, $\lambda_{t,n}^{int,ex}$) are used. Eq. 11 assures
that the power balance inside the community is satisfied, which is based on
the community-based market concept presented in [24]. Eq. 12 takes care of the
power balance of the internal and external power exchange with the power flows
of each member of the REC and Eq. 13 represents the power balance at each
users level.

$\sum_{n\in N}(Q^{im}_{t,n}\ -\ Q^{ex}_{t,n})\ =\ 0$ (11) $p_{t,n}\ -\
Q^{im}_{t,n}\ -\ P^{im}_{t,n}\ +\ Q^{ex}_{t,n}\ +\ P^{ex}_{t,n}\ =\ 0$ (12)
$p_{t,n}\ =\ l_{t,n}\ +\ p^{hp}_{t,n}\ +\ p^{cool}_{t,n}\ +\ p^{ev}_{t,n}\ -\
p^{pv}_{t,n}\ +\ p^{ch}_{t,n}\cdot y^{ch}_{t,n}\ -\ p^{disch}_{t,n}\cdot
y^{disch}_{t,n}$ (13)

With $p_{t,n}$ being the resulting power balance, $l_{t,n}$ the power demand,
$p^{hp}_{t,n}$ the heat demand, $p^{cool}_{t,n}$ the cooling demand,
$p^{ev}_{t,n}$ the EV charger demand, $p^{pv}_{t,n}$ the PV power production,
$p^{ch}_{t,n}$ the charging power of the battery and $p^{disch}_{t,n}$ the
discharging power of the battery. Heating and cooling loads of the heat pump
and electric vehicle all have their own power balance, shown in Eq. 14. Their
demand $d^{j}_{d,n}$ has to be satisfied on a daily basis by optimizing the
hourly usage of the assets taking in consideration the availability
$Y^{j}_{i,n}$ of the asset. Eq. 15 ensures that the maximum power of each
assets is respected.

$\sum_{i\in I^{d}}\ (p^{j}_{i,n}\cdot Y^{j}_{i,n})\ =\ d^{j}_{d,n}$ (14)
$p^{j}_{t,n}\ \leq\ m^{j}_{n}$ (15)

Eq. 16 sets the power output of the PV installation based on the normalized
electricity production from PV $G_{t}$ and its capacity $cap_{pv,n}$.

$p_{t,n}^{pv}\ =\ cap_{pv,n}\cdot G_{t}$ (16)

Eq. 17 represents the energy balance of the battery: $e_{t,n}$ is the energy
content of the BESS, $\eta^{ch}$ its charging efficiency and $\eta^{disch}$
its discharging efficiency. The inclusion of binary variables $y^{ch}_{t,n}$
and $y^{disch}_{t,n}$ ensures that the battery is not charging and discharging
at the same time (Eq. 18).

$e_{t+1,n}\ =\ e_{t,n}\ +\ \Delta t\cdot(y^{ch}_{t,n}\cdot
p^{ch}_{t,n}\cdot\eta^{ch}\ -\ y^{disch}_{t,n}\cdot
p^{disch}_{t,n}/\eta^{disch})$ (17) $y^{ch}_{t,n}\ +\ y^{disch}_{t,n}\ \leq\
1$ (18)

In addition boundaries for the charging and discharging powers of the battery
based on maximum C-rates are introduced:

$p^{ch}_{t,n}\ \leq\ \overline{p^{ch}}\cdot cap_{batt,n}$ (19)
$p^{disch}_{t,n}\ \leq\ \overline{p^{disch}}\cdot cap_{batt,n}$ (20)

as well as a minimum and maximum state-of-charge:

$SOC_{min}\cdot cap_{batt,n}\ \leq\ e_{t,n}\ \leq\ cap_{batt,n}$ (21)

##  4 Results

Results are presented in boxplots in order to make the comparison between the
156 scenarios more readable. To interpret the results one needs to compare the
same points between a group of boxplots (minimum with minimum, median with
median, etc.), as such points represent the same set of scenarios. Three of
the scenarios’ categories results - capex, tariff and investment - are
presented in separate groups of boxplots in order to assess their individual
impact on the KPIs. Additionally, the variation between different scenarios in
the technology scenario set has a large impact on the KPIs. This is the reason
why when presenting the results, the technology scenario set of 4 scenarios
are presented alongside each of the three previously mentioned scenario sets.
Finally, internal price scenarios apply only within the third investment
scenario, the REC.

###  4.1 CAPEX scenarios results

The comparison between different CAPEX for PV and BESS on the total annualized
cost is shown in Figure 1. Predictably, lower investment cost results in lower
total cost of electricity, but their impact is lower compared to the one of
the different electricity tariffs.

Figure 1: Total annualized cost per kWh - Capex scenarios comparison

Moreover, lower prices for assets lead to higher PV capacities installed (see
Figure 14), and also in this case we can see that the optimal size of PV
increase with the electrification of heating and transportation. BESS will
benefit even more than PV of a reduction of investment cost because current
prices (CAPEX scenario 1) will result almost every time in non installing any
storage system as optimal choice (see Figure 14) and BESS become more cost-
efficient in absence of flexibility in the system. As seen in previous
scenarios, higher installation of PV (Capex scenario 3) will decrease self-
consumption ratio (Figure 16) and increase self-sufficiency ratio (Figure 16).
Increase of BESS capacities, which also happen mostly for the third Capex
scenario, should theoretically increase both KPIs. However, their capacities
are relatively smaller than PV ones and therefore they do not have a large
impact on the results.

###  4.2 Tariff scenarios results

The total annualized cost per kWh consumed compared by tariff is shown in
Figure 2, where we can see that increasing the amount of capacity-dependent
component in the electricity tariff will reduce electricity cost. The smaller
variability between results in each tariff scenario compared to other scenario
comparison (investment and technology scenarios), indicates that tariffs are
one of the main parameter affecting the results.

Figure 2: Total annualized cost per kWh - Tariff scenarios comparison

Regarding PV installation with different tariffs, we observe that capacity
tariffs will decrease the optimal capacity installed (see Figure. 10). This
confirms that the size of the PV installation is proportional to the total
electricity demand as the capacity installed per consumption unit is very
similar for each technology scenario. For what it concerns batteries
installation, we can see that the optimal installed capacities follow a
different tendency than what was seen for PV: BESS are chosen almost only when
a capacity tariff is implemented (see Figure 10). For the investment
scenarios, a lack of flexibility in the system will also increase the amount
of BESS installed. Capacity tariffs also increase self-consumption compared to
more volumetric tariffs (Figure 11) due to less PV and more BESS present in
the system. On the other hand, for the same reasons, self-sufficiency ratio
decreases when the tariff is moving towards a capacity-based one (Figure 12)

###  4.3 Investment scenarios results

In general, independently of the technology scenarios the community investment
option always results in a better economic result. As presented in Figure 3 in
the case where investments are made, having more flexible assets (hence,
higher electricity consumption) leads to lower electricity cost per kWh
consumed. It also can be seen, that i) the variability of results (height of
the boxes) increase with higher penetration of flexible assets and ii)
investing in PV and BESS leads to a lower annualized total cost per kWh
compared to the BAU scenarios.

Figure 3: Total annualized cost per kWh - Investment scenarios comparison

PV capacity installed per consumption unit stays very similar for each
technology scenario, i.e. a higher electricity consumption (HP + EV scenario)
will increase the PV capacity installed for optimal solutions (see Figure 6).
It should be also noted that community investment scenarios allow the highest
PV and BESS integration. However, the cost-efficiency of PV and BESS follow
different trends in relation with the amount of existent flexibility in the
system. Higher capacity of BESS is more cost-efficient when less flexibility
is already available in the system (see Figure 6) while the opposite applies
for PV. Concerning the last two KPIs, the self-consumption ratio balances out
between technology scenarios (see Figure 8) while the self-sufficiency ratio
increases due to higher flexibility assets and PV penetration (see Figure 8).
For both KPIs, the community scenarios give the best results.

###  4.4 Internal price scenarios results

The impact on varying the internal energy exchange price is shown in Figure 4.
We can see how a higher cost for internal exchange will reduce the already
small gain, maximum 6 % at best on the annualized cost per kWh consumed, of
the REC over the individually optimized buildings.

Figure 4: Internal cost impact

##  5 Discussion

Investments in DER such as PV and storage systems such as batteries generate a
positive business case for the users. The creation of a REC always results in
a cost reduction compared to the reference case, with a reduction varying from
10% to 26 %. Similar numbers were found in an equivalent study in the Austrian
context [25], where the creation of a REC becomes eventually the economical
best solution for all the scenarios included. However, the economic advantage
only slightly increase with the creation of a REC compared to individual
investment and operation. RECs are able to decrease the annualised total cost
per energy consumed of at maximum 6 % compared to single user of an optimized
building. This is mainly achieved by introducing the possibility to exchange
energy between prosumers. On top of this one also needs to considers the IT
and IoT costs which can completely erase this small gain. Subsidies might help
but can create unjust societal situations that have to be studied from an
entire system perspective.

On site DER production for countries with such a high population density and
low levels of direct solar radiation, like Belgium, can expect limitations for
RECs related to be a community of place (where there is a proximity
constraint) and not of interest (where the proximity to the energy generation
assets does not necessarily play a role). This will limit technological
options for renewable self production of energy to PV. Moreover, reaching high
levels of self-sufficiency and self-consumption requires high levels of
electrification of multiple energy vectors, like heat and transport.

With the electricity prices for Flanders, the switch towards a larger power
component in the electricity bill results in a lower final cost for the users,
especially in the presence of optimal control of BESS or a HP and EV charger.
This is achieved by the ability to reduce the peak consumption, which is also
a positive outcome for the network operator. However, our findings show that
in this situation it is more convenient to install smaller capacities of PV
compared to traditional volumetric tariffs. This reduces the potential of RECs
to add RES generation capacity and limits their impact in the decarbonsation
of the energy system. On the other hand, a move towards a more capacity-based
tariff will trigger more investment into BESS, as found by Radl et al. [9] for
other European countries. In general, a more dynamic pricing mechanism would
be beneficial for RECs, as highlighted by [26], where the higher profit is
reached when real-time pricing is used.

One of the main limitation of the optimization model developed in this study
is that it works with a perfect foresight of the input profiles - consumption
and solar radiation - which take out all the real-life uncertainties. Possible
future work could be the introduction of stochastic input parameters to
further handle these uncertainties. Another difficulty encountered in this
work is to create a general model for REC because it could have all sorts of
prosumers/consumers and regulatory conditions. The consultation process whit
stakeholders allowed us to include typical diversity that can be found in
Flanders but efforts have to be invested in the creation of typologies of
RECs, which would allow the diversity necessary to conduct system wide
analysis.

##  6 Conclusions

The creation of a REC is always outperforming the other scenarios in both
economical and renewable energy penetration KPIs. However, there is never a
substantial economical advantage for REC over individual smart-houses with own
electricity generation assets. Moreover, there are still a lot of
uncertainties on the regulation of REC in Flanders and how peer-to-peer
exchange will work, which could potentially shift the benefits towards one
scenario or the other. In addition to that, the cost for setting up a REC,
both administrative and technical costs, are not included in this or any other
similar analysis. Hence, the small gain of constituting a REC could
potentially be erased by these additional costs. The shift towards a capacity
tariff would help the uptake of BESS as they will become more cost-efficient
due to their ability to shift peak demand. This will correspond to less PV
installed to reach the optimal situation compared to volumetric tariff.
Regarding the total cost comparison between volumetric and capacity-based
tariff, the latter gives the best results and in general are one of the two
most influential factors on the final cost of electricity. The other very
impactful factor on the annualised cost of energy is the amount of flexibility
present in the system. In fact, electrification of heat and transport will add
more possibilities to reduce costs in a smart energy system. The consequent
increase of electrical demand will also make PV installations more cost-
efficient with respect to situation where heat and transport are not powered
by electricity.

## Acknowledgements

This project has received funding from the European Union’s Horizon 2020
research and innovation program under grant agreement No 824342 as well as
from VLAIO in the ICON project ROLECS (reference HBC.2018.0527) and the
project MAMUET (grant number HBC.2018.0529).

##  7 CRediT authorship contribution statement

Alex Felice: Conceptualization, Methodology, Validation, Formal analysis,
Investigation, Data Curation, Software, Writing - Original Draft, Writing -
Review & Editing, Visualization Lucija Rakocevic: Conceptualization,
Methodology, Writing - Review & Editing. Leen Peeters: Resources, Project
administration, Funding acquisition. Maarten Messagie: Resources, Project
administration, Funding acquisition. Thierry Coosemans: Resources, Project
administration, Funding acquisition. Luis Ramirez Camargo: Conceptualization,
Methodology, Writing - Review & Editing.

## References

  * [1] EC, Directive (EU) 2018/2001 of the European Parliament and of the Council of 11 December 2018 on the promotion of the use of energy from renewable sources, Official Journal of the European Union L 328 (April 2009) (2018) 82–209.
  * [2] C. Alaton, F. Tounquet, Energy communities in the clean energy package - Publications Office of the EU, Tech. rep., European Commision (2020).
URL https://op.europa.eu/en/publication-
detail/-/publication/4b7d5144-91c9-11eb-b85c-01aa75ed71a1/language-
en%0Ahttps://op.eur

  * [3] C. E. Hoicka, J. Lowitzsch, M. C. Brisbois, A. Kumar, L. Ramirez Camargo, Implementing a just renewable energy transition: Policy advice for transposing the new European rules for renewable energy communities, Energy Policy 156 (2021) 112435\.  doi:10.1016/j.enpol.2021.112435.
URL https://linkinghub.elsevier.com/retrieve/pii/S0301421521003050

  * [4] C. Inês, P. L. Guilherme, M. G. Esther, G. Swantje, H. Stephen, H. Lars, Regulatory challenges and opportunities for collective renewable energy prosumers in the EU, Energy Policy 138 (2020) 111212.  doi:10.1016/j.enpol.2019.111212.
  * [5] V. Brummer, Community energy – benefits and barriers: A comparative literature review of Community Energy in the UK, Germany and the USA, the benefits it provides for society and the barriers it faces (oct 2018).  doi:10.1016/j.rser.2018.06.013.
  * [6] P. D. Conradie, O. De Ruyck, J. Saldien, K. Ponnet, Who wants to join a renewable energy community in Flanders? Applying an extended model of Theory of Planned Behaviour to understand intent to participate, Energy Policy 151 (2021) 112121.  doi:10.1016/j.enpol.2020.112121.
  * [7] T. Bauwens, P. Devine-Wright, Positive energies? An empirical study of community energy participation and attitudes to renewable energy, Energy Policy 118 (2018) 612–625.  doi:10.1016/j.enpol.2018.03.062.
  * [8] T. Bauwens, Analyzing the determinants of the size of investments by community renewable energy members: Findings and policy implications from Flanders, Energy Policy 129 (2019) 841–852.  doi:10.1016/j.enpol.2019.02.067.
  * [9] J. Radl, A. Fleischhacker, F. H. Revheim, G. Lettner, H. Auer, Comparison of Profitability of PV Electricity Sharing in Renewable Energy Communities in Selected European Countries, Energies 13 (19) (2020) 5007.  doi:10.3390/en13195007.
URL www.mdpi.com/journal/energieshttps://www.mdpi.com/1996-1073/13/19/5007

  * [10] G. Fridgen, M. Kahlen, W. Ketter, A. Rieger, M. Thimmel, One rate does not fit all: An empirical analysis of electricity tariffs for residential microgrids, Applied Energy 210 (2018) 800–814.  doi:10.1016/j.apenergy.2017.08.138.
  * [11] M. Ansarin, Y. Ghiassi-Farrokhfal, W. Ketter, J. Collins, Cross-subsidies among residential electricity prosumers from tariff design and metering infrastructure, Energy Policy 145 (2020) 111736.  doi:10.1016/j.enpol.2020.111736.
  * [12] C. Clastres, J. Percebois, O. Rebenaque, B. Solier, Cross subsidies across electricity network users from renewable self-consumption, Utilities Policy 59 (2019) 100925.  doi:10.1016/j.jup.2019.100925.
  * [13] T. Schittekatte, I. Momber, L. Meeus, Future-proof tariff design: Recovering sunk grid costs in a world where consumers are pushing back, Energy Economics 70 (2018) 484–498.  doi:10.1016/j.eneco.2018.01.028.
URL https://cadmus.eui.eu//handle/1814/53324

  * [14] I. Abada, A. Ehrenmann, X. Lambin, Unintended consequences: The snowball effect of energy communities, Energy Policy 143 (2020) 111597.  doi:10.1016/j.enpol.2020.111597.
  * [15] T. Weckesser, D. F. Dominković, E. M. Blomgren, A. Schledorn, H. Madsen, Renewable Energy Communities: Optimal sizing and distribution grid impact of photo-voltaics and battery storage, Applied Energy 301 (2021) 117408.  doi:10.1016/j.apenergy.2021.117408.
  * [16] D. Fioriti, A. Frangioni, D. Poli, Optimal sizing of energy communities with fair revenue sharing and exit clauses: Value, role and business model of aggregators and users, Applied Energy 299 (2021) 117328.  doi:10.1016/j.apenergy.2021.117328.
URL https://linkinghub.elsevier.com/retrieve/pii/S0306261921007376

  * [17] F. Braeuer, M. Kleinebrahm, E. Naber, F. Scheller, R. McKenna, Optimal system design for energy communities in multi-family buildings: the case of the German Tenant Electricity Law, Applied Energy 305 (2022) 117884.  doi:10.1016/j.apenergy.2021.117884.
URL https://linkinghub.elsevier.com/retrieve/pii/S0306261921012009

  * [18] B. Fina, A. Fleischhacker, H. Auer, G. Lettner, Economic Assessment and Business Models of Rooftop Photovoltaic Systems in Multiapartment Buildings: Case Studies for Austria and Germany, Journal of Renewable Energy 2018 (2018) 1–16.  doi:10.1155/2018/9759680.
  * [19] F. Scheller, B. Burgenmeister, H. Kondziella, S. Kühne, D. G. Reichelt, T. Bruckner, Towards integrated multi-modal municipal energy systems: An actor-oriented optimization approach, Applied Energy 228 (2018) 2009–2023.  doi:10.1016/j.apenergy.2018.07.027.
  * [20] A. Felice, L. Rakocevic, L. Peeters, M. Messagie, T. Coosemans, L. R. Camargo, An assessment of operational economic benefits of renewable energy communities in Belgium, Journal of Physics: Conference Series 2042 (1) (2021) 012033.  doi:10.1088/1742-6596/2042/1/012033.
URL https://iopscience.iop.org/article/10.1088/1742-6596/2042/1/012033/meta

  * [21] TRNSYS, Transient System Simulation Tool, Http://Www.Trnsys.Com/ (2013).
URL http://www.trnsys.com/

  * [22] VREG, Tariefmethodologie 21-24: Nieuwe tariefstructuur vanaf 2022, Tech. rep., VREG (2020).
URL
https://www.vreg.be/sites/default/files/Tariefmethodologie/2021-2024/publieke_toelichting_consultatie_tm_21-24.pdf

  * [23] Eurostat, Electricity price statistics - Statistics Explained., European Commission (November) (2020) 1–12.
URL https://ec.europa.eu/eurostat/statistics-
explained/index.php/Electricity_price_statistics

  * [24] T. Sousa, T. Soares, P. Pinson, F. Moret, T. Baroche, E. Sorin, Peer-to-peer and community-based markets: A comprehensive review (apr 2019).  arXiv:1810.09859, doi:10.1016/j.rser.2019.01.036.
  * [25] A. Cosic, M. Stadler, M. Mansoor, M. Zellinger, Mixed-integer linear programming based optimization strategies for renewable energy communities, Energy 237 (2021) 121559.  doi:10.1016/J.ENERGY.2021.121559.
  * [26] E. Fernandez, M. J. Hossain, K. Mahmud, M. S. H. Nizami, M. Kashif, A Bi-level optimization-based community energy management system for optimal energy sharing and trading among peers, Journal of Cleaner Production 279 (2021) 123254\.  doi:10.1016/J.JCLEPRO.2020.123254.

##  Appendix A Supplementary material

Figure 5: PV capacity installed per MWh consumed - Investment scenarios
comparison

Figure 6: BESS capacity installed per MWh consumed - Investment scenarios
comparison

Figure 7: SCR - Investment scenarios comparison

Figure 8: SSR - Investment scenarios comparison

Figure 9: PV capacity installed per MWh consumed - Tariff scenarios comparison

Figure 10: BESS capacity installed per MWh consumed - Tariff scenarios
comparison Figure 11: SCR - Tariff scenarios comparison Figure 12: SSR -
Tariff scenarios comparison

Figure 13: PV capacity installed per MWh consumed- Capex scenarios comparison

Figure 14: BESS capacity installed per MWh consumed - Capex scenarios
comparison

Figure 15: SCR - Capex scenarios comparison

Figure 16: SSR - Capex scenarios comparison