Dynamic Simulation Model of a Renewable Energy Community for

Small Municipalities

Francesco L. Cappiello

, Luca Cimmino

, Chiara Martone

and Maria Vicidomini

University of Naples Federico II, P.le Tecchio 80, 80125, Naples, Italy

University of Sannio, P. Roma 21, 82100, Benevento, Italy

Keywords: Renewable Energy Communities, Dynamic Simulation Models, Economic Analysis Based on Regulation,

Storage System.

Abstract: The presented paper describes the dynamic simulation model developed to predict the real time operation of

a Renewable Energy Community based on PV panels coupled with energy storage systems. The dynamic

model is able to evaluate the self-consumed energy of the community as well as the energy delivered to the

grid considering a real electric load of the community. The model is able to evaluate in detail the economic

feasibility of the plant, according to a comprehensive economic analysis, based on the Italian regulation.

TRNSYS software is used to model the included energy components. The model is applied to a suitable case

study, the municipality of Foiano di Val Fortore, located in the south of Italy. The main results of the presented

analysis highlight that the photovoltaic panels lead to a reduction of the primary energy consumption of the

renewable energy community by 32%. Due to incentives the achieved simple payback is extremely low. In

fact, when the energy storage system is not considered, the achieved simple payback is equal to 4.0 years.

When the PV panels are coupled with the energy storage system, the simple payback reaches the value of 13.5

years.

1 INTRODUCTION

The European Union (EU) is actively promoting the

energy transition process, focusing on substantial

reduction of greenhouse gas (GHG) emissions,

improvement of energy efficiency, and increase of

energy share from renewable energy sources (RESs)

(F. Calise, Vicidomini, Cappiello, & D’Accadia,

2021). In line with the EU long-term strategy for

achieving climate neutrality by 2050, significant

changes should be implemented both in the

production and final consumption stages (Gianaroli et

al., 2024).

In this regard, global renewable energy capacity

has increased over the recent years, accounting for

43% to the end of 2023 (F. Calise, Cappiello, Dentice

d'Accadia, & Vicidomini, 2020), mainly due to

growing of solar and wind-based plants. On the other

https://orcid.org/0000-0001-6292-686X

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https://orcid.org/0009-0008-2274-1316

https://orcid.org/0000-0003-2827-5092

side, energy sharing emerges as a key factor of the

decarbonization effort within the framework of the

circular economy, intending to provide the entire

population with environmental, economic and social

benefits (Sajjad Ahmed & Măgurean, 2024).

Following the focus on the active role of end-user

in the energy transition, EU encourages energy-

sharing models, such as Renewable Energy

Communities (RECs) (Lowitzsch, Hoicka, & van

Tulder, 2020). Introduced by the Renewable Energy

Directive (RED II) in EU legislation, RECs enhance

the gathering of local users to better align energy

demand with generation locally, thus alleviating the

strain on power grids (PG) (Volpato et al., 2024).

REC members, private citizens, local authorities or

small and medium enterprises, can hold the role of

consumers, producers or both, becoming prosumers

(Esposito et al., 2024). Optimal design of the

Cappiello, F. L., Cimmino, L., Martone, C. and Vicidomini, M.

Dynamic Simulation Model of a Renewable Energy Community for Small Municipalities.

DOI: 10.5220/0013464700003953

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 14th International Conference on Smart Cities and Green ICT Systems (SMARTGREENS 2025), pages 225-231

ISBN: 978-989-758-751-1; ISSN: 2184-4968

225

configuration, in terms of selected technologies and

members, contributes to the maximization of

associate advantages (Laurini, Bonvini, & Bracco,

2024). Furthermore, the inclusion of charging points

for electric mobility, as REC’s points of delivery,

allows to meet flexible management in order to

minimize the electric energy import and export to the

PG (Velkovski, Gjorgievski, Markovski, Cundeva, &

Markovska, 2024) simultaneously offering additional

services to citizens.

The immediate benefits of energy sharing are in

primary energy saving and reduction of

environmental impact related to REC’s members

consumptions. In addition, economic incentives are

also recognised on shared energy as determined by

the regulation context of some EU states, such as

Italy. Other countries, as France, Germany and Spain,

provide feed-in tariffs for electricity sold to the PG

(Belloni, Fioriti, & Poli, 2024). Finally, the use of

locally available RESs enhances the acceptability of

plants and boosts the direct economic impact on the

territory, also through the development of short

supply chains, with implications for employment.

Energy justice can also be addressed, by including

vulnerable end-users with an energy poverty

mitigation aim (Campagna, Rancilio, Radaelli, &

Merlo, 2024).

In this framework, the following paper addresses

the evaluation of the economic feasibility of a specific

REC implementing the dynamic simulation approach

for the evalutation of the shared renewable energy by

a detailed economic analysis based on the Italian

regulation context.

2 METHOD

In this section the layout of the investigated

renewable energy community (REC) and the dynamic

simulation model developed to assess the energy

performance of the PV plant in terms of self-

consumed energy and energy delivered to the grid, as

well as the economic feasibility of the plant, is

described.

2.1 Layout

The layout is very simple and is based on a PV field

connected to a system inverter/regulator, managing

the load of the community and the power production

of the solar field. However, in order to better

understand the performance of such REC, several

scenarios were investigated, namely:

• scenario A1: PV field without electric energy

storage system (ESS);

• scenario A2: PV field with ESS.

2.2 System Model

TRNSYS 18 was adopted to develop the dynamic

simulation model of the examined REC layouts.

TRNSYS is a reference and valid tool for the

academic community. The software is based on built-

in components, experimentally validated (Bordignon,

Emmi, Zarrella, & De Carli, 2021; Francesco L.

Cappiello, 2024; Francesco Liberato Cappiello &

Erhart, 2021; Klein et al., 2006; Testasecca, Catrini,

Beccali, & Piacentino, 2023), providing high

accuracy and reliability of the returned results in

terms of dynamic energy performance of solar

systems (Bordignon et al., 2021; Francesco L.

Cappiello, 2024; Francesco Liberato Cappiello &

Erhart, 2021; Klein et al., 2006; Testasecca et al.,

2023). The energy components of TRNSYS are

defined as Types and are based on detailed and

comprehensive models. In this work, in order to

simulate the presented scenarios, the following types

are adopted.

Photovoltaic Field Model. Type 94 simulates the PV

field of the REC using the so-called “four

parameters” model (Buonomano, Calise, d'Accadia,

& Vicidomini, 2018).

Lithium-Ion Battery Model. Type 47 simulates the

lithium-ion ESS according to the Shepherd model.

Note that his model is natively designed for

mimicking the performance of lead acid battery.

However, in this case the main parameters of the type

are customized in order to fit the performance of a

lithium-ion battery (Francesco Calise, Cappiello,

Cartenì, Dentice d’Accadia, & Vicidomini, 2019).

The model evaluates the discharging efficiency

according to battery conditions. Further details are

available in ref (Francesco Calise et al., 2019).

Regulator/Inverter Model. Type 48 models the

regulator/inverter for the optimal management of the

current exchanged among PV arrays, ESS, inverter

and community. Type 48 is used to mimic the

performance of a regulator/inverter converting the

DC into AC, before providing it to the electric grid

when the state of charge reaches the maximum value

or to the charging stations.

In the A1 and A2 scenarios, two distinct models have

been developed. These dynamic models are designed

to mimic the performance of the PV field and storage

system. They also assess the energy shared within the

SMEN 2025 - Special Session on Smart City and Smart Energy Networks

226

Figure 1: Daily load for a typical summer day (below) and a typical winter day (above).

Table 1: Main economic assumptions.

Paramete

Description Value Uni

Photovoltaic specific cost 1.400·(

el,PV,rated

)

- 0.075

XX €

LIB

Lithium-ion battery specific cost

200.0 (Shabbir Ahmed et al., 2018; F. Calise,

Cappiello, Cimmino, & Vicidomini, 2023)

€/kWh

Ord

Ordinary maintenance 3.0 %/yea

ext

Extraordinary maintenance (10

year) 20.0 %

toGrid

Electricity exporting price

0.060(https://www.mase.gov.it/comunicati/energia-

mase-

ubblicato-decreto-ce

, Italian Government)

€/kWh

fromGrid

Electricity purchasing cost

0.210

(https://www.mase.gov.it/comunicati/energia-

mase-

ubblicato-decreto-ce

, Italian Government)

€/kWh

feed

Feed in tariff due to REC policy (related to self-

consumed energy)

0.120

(https://www.mase.gov.it/comunicati/energia-

mase-

ubblicato-decreto-ce

, Italian Government)

€/kWh

feed(arera)

Feed in tariff due to ARERA

0.008

(https://www.mase.gov.it/comunicati/energia-

mase-

ubblicato-decreto-ce

, Italian Government)

€/kWh

inc,cap

Incentive due to REC policy (related to capital

cost)

40.0 (https://www.mase.gov.it/comunicati/energia-

mase-

ubblicato-decreto-ce

, Italian Government)

community and the PV energy production in relation

to the energy demand of the REC. For both scenarios,

an economic analysis was developed. In order to

perform the analysis, the scenarios were compared

with the reference system (RS), where the load is

totally balanced by the electric energy withdrawn

from the grid.

Concerning the proposed systems (A1 and A2),

according to REC policy, only a virtual electricity

self-consumption is considered

(https://www.mase.gov.it/comunicati/energia-mase-

pubblicato-decreto-cer, Italian Government). This

means that the total PV energy production is

delivered to the grid, and the load of the REC is

balanced withdrawing the electricity from the grid.

Then the self-consumed energy is assessed as the

difference between the PV energy production and the

community energy demand. The main economic

factors considered were: i) the unit cost of electricity

withdrawn from the grid j

fromGrid

; ii) the REC ordinary

management, maintenance and administration costs

Ord

; iii) the selling of the renewable electricity j

toGrid

;

iv) the feed in tariff j

feed

according to the Italian

regulation and the feed in tariff due to ARERA

feed(ARERA)

(https://www.mase.gov.it/comunicati/energia-mase-

pubblicato-decreto-cer, Italian Government).

Note

that for both scenarios, the economic analysis is

developed by means a suitable cash flow able to

evaluate the following economic indexes: the simple

Dynamic Simulation Model of a Renewable Energy Community for Small Municipalities

227

payback period (SPB), the net present value (NPV)

and the profit index (PI), assuming a lifetime of 20

years and a discount rate of 5%. To evaluate the

economic feasibility of the considered scenarios, the

capital costs of the PV field (scenario A1 and A2) and

energy storage system (scenario A2) are evaluated

considered the nominal capacity of the components,

[kW] and Cap

LIB

[kWh].

() ( )

0,075

1400·0·20

TOT PV LIB PV LIB

JJJ P Cap

−

+=+=

(1)

For each year, the yearly economic saving ΔC,

difference between the operating cost of the reference

and proposed system, is evaluated according two

incentive regulations, namely: ΔC

jfeed

and

ΔC

INC,CC.

This last economic saving considers

the feed in tariff

feed

reduced by half, because an incentive according

to the REC regulation, equal to 40% of the capital

cost, is expected.

()

,,self()

el f romGRID f romGRID Ord

jf eed el fr omGRID f romGRID

el toGRID toGRID el feed ARERA feed

Ej m

CE j

Ej Ej j



Δ= −



−−





(2)

()

,,self()

e l f r o mGRI D f ro mGRI D Or d

INC CC el fromGRID fromGRID

feed

el t oGRID t oGRID el f eed ARERA

Ej m

CEj

Ej Ej





Δ= −





−−











(3)

3 CASE STUDY

The case study selected for this research consist of the

municipality of Foiano di Val Fortore (F. Calise,

Cappiello, Cimmino, Dentice d'Accadia, &

Vicidomini, 2024) located in the south of Italy. In

particular, such small municipality includes 1 325

inhabitants (F. Calise et al., 2023). Figure 1 displays

the assumed load of the whole municipality: ranging

around 24 kW.

In the reference system the municipality load is

balanced by means of the electricity withdrawn from

the grid.

The proposed system 1 (A1) relies on the

foundation of a renewable energy community, where

a diffuse photovoltaic installation is performed. In

particular, an overall PV capacity of 50 kW is

installed. Note that since the diffuse photovoltaic

field installation only a virtual electricity self-

consume is considered. This concept is described in

the previous section.

The proposed system 2 (A2) is equal to the

proposed system 1, i.e. it is considered a renewable

energy community relying on diffuse photovoltaic

field installation. Note that in this case an overall PV

capacity of 50 kW is considered. Moreover, such

arrangement also includes a district electricity energy

lithium-ion battery of 20 kWh. Also, in this case the

virtual electricity self-consumption is considered.

Table 1 summarizes the main cost figures and

assumption regarding the economic analysis. The PV

plant is shutdown 2 days per month, due to

maintenance. This assumption is performed for both

the proposed systems. Note that an yearly degradation

by 2% through the whole life time of the PV field is

considered.

4 RESULTS

This section deals with the results achieved by this

work.

Table 2 summarizes the yearly results. As

expected, the installation of the PV fields leads to a

significant reduction of the primary energy

consumption of the municipality. In particular, a

reduction by 32 % of the primary energy of the

municipality

is achieved for SP1 (see PES Table 2).

Table 2: Yearly results for A1.

Parameter

RS PS

Unit

Value

el,

romGRID

216.57 158.19 MWh/y

el,toGRID

- 11.61 MWh/y

el,P

- 71.42 MWh/y

el,sel

- 58.38 MWh/y

470.81 318.66 MWh/y

el,sel

el,LOAD

- 26.96 %

el,sel

el,P

- 81.74 %

ΔP

- 152.15 MWh/y

- 32.32 %

SPB (feed) - 5.30 years

NPV (feed) - 56.10

€

PI (feed) -1.10 -

SPB (feed+inc) - 4.00 years

NPV (feed+inc) - 52.20

€

PI (feed+inc) -1.70 -

Note that the self-consumed energy (E

el,self

el,LOAD

)

balances almost 27.0% of the load of the

municipality,

Table 2. This result is due to the fact that

the power production occurs only during the central

part of the day balancing only a limited part of the

district daily load (see Figure 2). Note that the district

is able to self-consume the majority of the PV

production, i.e.

el,self

el,PV

almost equal to 87%, Table 2.

These results are quite expected. From the economic

point of view, the REC policy is able to make this

investment profitable. The scenario, where only the

feed in tariffs are considered, leads to simple payback

of 5.30 years, with a NPV of 56 k€. The scenario,

where both the feed in tariff and the capital cost

SMEN 2025 - Special Session on Smart City and Smart Energy Networks

228

incentives are considered, achieve the better results

with a limited payback period of 4.0 years and NPV

of 52.2 k€. Then, the policy combining the reduced

feed in tariff with the capital cost incentive is the

better solution.

Figure 2: Dynamic results for A1.

The proposed system 2 (A2) is able to furterly reduce

the primary energy consumption of the district due to

the electric energy storage system. In fact, for A2 the

REC is able to reduce the primary energy

consumption by 31%, see PES

Table 3. Note that the

battery increases by 3% the self-consumed energy

ratio (

el,self

el,LOAD

), which passes from (26% in A1

Table 2) to 29% in A2 Table 3. This slight

enhancement is mainly due to the battery limited

capacity. In fact, because the high capital cost of such

technology a small battery is installed. The result is

furtherly confirmed by Figure 3. In fact, the battery is

able to handle only a limited amount of the excess of

renewable electricity, i.e. 4.22 kW out of 10.91 kW,

Figure 3.

Figure 3: Dynamic results for A2.

Table 3: Yearly results for A2.

Parameter

RS PS

Unit

Value

el,

romGRID

216.57 153.73 MWh/y

el,toGRID

- 4.45 MWh/y

el,P

- 71.42 MWh/y

el,sel

- 62.85 MWh/y

el,

romLIB

- 6.96 MWh/y

el,toLIB

- 4.84 MWh/y

el,

romLIB

el,LOAD

-3.21 %

el,toLIB

el,P

-6.77 %

el,sel

el,LOAD

- 29.02 %

el,sel

el,P

- 88.00 %

470.81 324.52 MWh/y

ΔP

- 146.29 MWh/y

- 31.07 %

SPB (inc) - 12.40 years

NPV (inc) -2.97

€

PI (inc) -0.05 -

SPB (inc+inc,cap) - 13.50 years

NPV (inc+inc,cap) -0.01

€

PI (inc+inc,cap) -0.00 -

The battery adoption leads to a worsening of the

economic performance if compared with A1 ( i.e. the

layout without the battery). This result is related with

the fact that the battery has a very high specific cost,

leading to average economic results. In other words,

the increase in the capital cost is not balanced by the

reduction of the operative cost due to the increased

self-consumed energy. As for A2, the better policy

relies on the full feed in tariffs policy without any

incentive on the investment. This result is mainly due

to the fact that the policy regarding the feed in tariff

is able to maximize the revenue due to the increase in

the self-consumed energy because the battery

adoption.

5 CONCLUSIONS

This paper deals with the analyses of the energy

performance and economic performance of a

renewable energy community. The simulation model

of the renewable energy community is developed in

TRNSYS environment. The small town of Foiano di

Val Fortore is selected as suitable case study. In

particular, it is supposed to found a renewable energy

community relying on diffuse photovoltaic

installation. For this reason, in this case the virtual

electricity self-consumption is considered. According

to this policy all the electricity produced by the

photovoltaic field is exported to the grid, the load of

the district is balanced by the electricity withdrawn

from the district. The self-consumed energy is

considered equal to the difference between the

electricity delivered to the grid and withdrawn from

Dynamic Simulation Model of a Renewable Energy Community for Small Municipalities

229

the grid. Note that the assessment of the self-

consumed energy is crucial in the framework of the

renewable energy community, since the feed in tariff

rewards the electricity self-consumed. Two scenarios

are considered, one based on the renewable energy

community relying only on the diffuse photovoltaic

installation. The second one is based on an energy

community adopting the diffuse photovoltaic

installation and a lithium-ion battery.

The main findings of this research are condensed

below.

• The photovoltaic adoption leads to a

reduction of the primary energy

consumption of the renewable energy

community by 32%.

• The self-consumed energy balances 26% of

municipality load for the scenario relying

only on photovoltaic. The self-consumed

energy matches almost 29% of the load of

the municipality for the scenario adopting

photovoltaic and battery.

• The renewable energy community policy is

useful in making such investments very

profitable. In fact, due to incentives the

achieved simple payback is extremely

limited. The first scenario achieves a simple

payback of 4.0 years and the second scenario

reaches a simple payback of 13.5 years.

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