Indicators and Representation Tools to Measure the Technical-Economic Feasibility of a Renewable Energy Community. The Case Study of Villar Pellice (Italy)

Increasing global energy demand caused by social progress is in contradiction with the lack of energy resources [1]. The challenge of overcoming the climate change and the global warming is faced by many cities through projects and urban policies. Both focus on mitigation measures to reduce greenhouse gases (GHG) emissions, introducing the concept of Resilient Cities and Communities: a city prepared to absorb and recover from any shock or stress, adapting on the continual change [2]. To plan sustainable cities, the application of models improves the livability of a territory, for example in [3] a useful tool is proposed for designing a comfort model by optimizing energy consumption, while [4] provides a methodology that incorporates mitigation of the UHI phenomenon in urban planning. Lots of new energy policies are favoring renewable energy sources which involves in new specific challenges. The policy makers interested need to be able to drive the process towards an increase of the share of RES, at regional level where local action plans must be coordinated and supported by common strategies [5]. Nowadays, it is possible to live in an isolated environment, such as small islands and rural areas, in a sustainable way. Thanks to RES availability, a more sustainable environmental system can be obtained, implementing local energy production, according to the local energy demand, reducing energy cost and ensuring an energy independence [6].

It is crucial for EC to rely on the political support of the local governments and institutions to integrate energy planning at territorial scale with the existing urban and territorial plans [7]. The design of an energy community can facilitate energy planning on a territorial scale: in this way it is possible to implement the production where there is an energy demand; always considering that it is of primary importance to act on the reduction of energy consumption in all sectors (residential, industrial, transport).

2.1 Research objectives

This study describes a methodology to measure a new Renewable Energy Community through the case study of the municipality of Villar Pellice (TO, Italy). The purpose is to develop a flexible and replicable methodology that can be easily adapted to other case studies at different contexts and scales. Furthermore, the study quantifies the effects of cost-benefits analysis among scenarios, assuming different forms of stakeholders’ aggregations and energy efficiency interventions, including establishing an REC [8]. From the identification of the territorial peculiarities, the place-based methodology allows to evaluate different energy profiles and combine the energy demand and supply where there met.

The place-based methodology used in this work allows to consider several energy-related variables at different scales. This methodology can include all the constraints present on a territory: technical, environmental, social, and regulatory/legislative type [9]. This analysis is carried out in three phases, as shown in Figure 3:

1) definition of the hourly energy consumption and production profiles for each REC member, according to the category of end user and RES technological system;

2) assessment of the energy balance and the energy performance through a set of energy fluxes and energy indicators, respectively to compare all scenarios hypothesized;

3) cost-benefits analysis, considering investment costs, energy costs, and pay-back time, including the economic incentive for REC configuration, comparing different scenarios of intervention.

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Figure 3. Place-based methodology

4.1 Energy data source

The annual and monthly energy consumption data of the municipality refer to the year 2017. The reference database for the annual energy consumption at territorial scale is the SIATEL database (Local Authority Tax Registry Interchange System(https://puntofisco.agenziaentrate.it/PuntoFiscoHome/LogonMatrice.jsp). The telematic system allows the active exchange of personal and tax information between the central and local Italian public administration. Therefore, it was possible to consult the database of the financial administration relating to the energy expenditure of the different categories of end users present in the municipal area; sensitive data have been deleted by the municipal authority. The monthly and hourly energy consumption of residential users have been provided by ACEA Pinerolese Spa. Considering a sample of about 380-470 residential users located in the Pinerolo area, data refers to a typical residential user, calculated for 12 typical days of the year 2017, considering seasonal and weekly differences. According to the Italian census database (ISTAT 2011, https://www.istat.it), it corresponds to a family of 2.15 components, in a 93.78 m² dwelling, located at an altitude of 581 m a.s.l. and in climatic zone E, with 2,829 HDD. The monthly and hourly energy data of the Nuova Crumière company come from the bills, as provided by the Energy Manager of the company.

All consumers can access data of their electricity supplies, including historical consumption, technical and contractual information through various telematic methods, based on the provisions of ARERA (N.L. 205/2017). After registering on the website of the electric distribution system operator in the area, for installed powers above 35 kW and new generation electronic meters, all end users can access their profiles and download the load curves of the electricity consumption in quarterly detail. At national level, by authenticating via digital identity, it is also possible to access the "Consumer Portal" managed by the Acquirente Unico (https://www.consumienergia.it/portaleConsumi/), which makes available energy data at different details, depending on the type of meter. If the supply is equipped with an electronic counter, it is possible to download data by time band.

4.2 Hourly energy consumption profiles

The hourly energy consumption of the municipal buildings was assessed starting with the available annual and monthly data, based on SIATEL database and energy bills, respectively. According to the holiday calendar relating to the examined year, the monthly energy consumption of the Town Hall (TH) and the Warehouse (WA) was hourly distributed, considering the daily opening hours and use of these facilities. The hourly energy consumptions of the municipal Public Lighting (PL) are based on monthly available data of 2017. Starting from the conventional switch-on and switch-off times established for municipalities located in the north-west area of the country, as indicated by the national energy authority ARERA (ARG/let/29/08), the hours of utilization were calculated, and subsequently, the respective switch-on time profile was assigned to every day of the year. The energy profile of the residential user of the Social Housing (SH) was evaluated starting from the annual data provided by the SIATEL database. From this, the average annual electric consumption of citizens residing in the municipality in 2017 was obtained. This latter coincided with the annual energy consumption of a typical residential users in Villar Pellice, as its characteristics are in line with those of the typical residential user and customer evaluated for the monthly and hourly consumption profiles provided by ACEA Pinerolese SpA (see par. 4.1).

4.3 Hourly energy production profiles

The evaluation of the roof integrated PV panels to be installed has been made considering the available area without shadings, the technological characteristic of the PV system and the solar irradiation of the examined year. Relying on the PVGIS tool (https://ec.europa.eu/jrc/en/pvgis), it is possible to calculate the hourly energy production in every hour of the year. The tool needs the following constraints: location (latitude, longitude, and elevation), radiation database, year of evaluation (from 2005 to 2016), PV mount type (fixed, vertical axis, inclined axis and two axes), slope of the panel, azimuth of the building, PV technology, nominal power of the PV system and system loses. For the case study analyzed, all the prosumers installed PV panels of polysilicon technology and fixed mounting type, with system losses estimated at 14%. The climatic and solar radiation data refer to the radiation database PVGIS-SARAH for the year 2016. Table 3 presents the characteristics for each of the PV plants that are expected to be installed on the roof of the selected building.

Table 3. Input data PVGIS

4.4 Energy flows in REC configuration

Figure 4 explains the energy flows that occurs in case of the REC configuration. In this scheme, the total amount of local energy production from new RES power plants is the Total Production (TP), and the Total Consumption (TC) refers to the energy demand of all EC members.

The Self-Consumption (SC) is the share of energy that is instantly self-consumed by each prosumer and it can be calculated according to Eq. (2) and (4).

The Uncovered Demand (UD) is the share of energy consumption that is not satisfied by the local production and it must withdraw from the national grid, as it shown in Eq. (1).

if TC ≥ TP and OP=0

UD [kWh]= Total Consumption-Total Production     (1)

SC [kWh]= Total Consumption-Uncovered Demand    (2)

The Over Production (OP) is the share of energy that is not instantly self-consumed because the energy generated is greater than the energy demand, as it expressed in Eq. (3):

if TP ≥ TC and UD=0

OP [kWh]= Total Production-Total Consumption    (3)

SC [kWh]= Total Production- Over Production    (4)

The Collective Self Consumption (CSC) is the share of energy that is exchanged among the REC members. It corresponds to the minimum between the energy fed into the national grid by all the new RES plants of the REC (OP) and the energy withdrawn from the national grid by all the members of the REC (UD), in each hourly period.

CSC = MIN (OP: UD)    (5)

The still Over Production (sOP) is the share of energy production still available after the withdrawn from EC members (CSC), and it can be sold to the national grid.

The still Uncovered Demand (sUD) is the share of energy consumption that has not be satisfied by the Self-Consumption (SC), nor by the collective Self-Consumption withdraw (CSC), and necessarily has to be withdrawn from the national grid.

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Figure 4. Hourly energy flows in the REC configuration

4.5 Energy performance indicators

Determining the flow of energy carriers is one of the first step to propose changes in the management of energy locally. Once renewable technologies that can meet current demands are identified, it is possible to create local energy scenarios and by using sustainability indicators proposed options can be formulated [10]. In assessing the potential of PV-battery system, Luthander et al. [11] report the importance of the daily and seasonal matching between local energy production and consumption, using two indicators to express the load matching potential.

These are the Self-Sufficiency Index (SSI) and the Self-Consumption Index (SCI). Both can be used to assess the energy performance of the REC interconnected to the national grid. The Self-Sufficiency Index (SSI) indicated the share of locally self-consumed energy out of the total energy consumption, and it is calculated with Eq. (6). The Self-Consumption Index (SCI) is defined as the share of locally self-consumed energy out to the total energy production by RES, as described with Eq. (7). Both indexes are calculated summarizing the Self-Consumption (SC), the collective Self-Consumption (CSC), the Total Consumption (TC) and the Total Production (TP) of all the REC members.

$S S I=\quad(S C+C S C) / T C$      (6)

$S C I=(S C+C S C) / T P$      (7)

The Regional Law of the Piedmont Region (R.L. 12/2018) requires a minimum threshold of the annual self-consumption index (SCI) for Energy Community configurations that correspond to 70%. Choosing the intervention to be implemented that best contributes to reaching acceptable thresholds for both indices, implies economic analysis and cost-optimal assessment.

4.6 Cost-benefits analysis

The aim of the cost-benefits analysis is to highlight the economic benefits for each stakeholder, in each supposed scenario, to identify the one that ensures economic benefits for all. The cost-optimal analysis allows to compare the economic implication of the different types of intervention, defining the optimal level of performance as a function of costs; in this work, the energy performance concerns the contribution to self-sufficiency and self-consumption indexes improvement, and the economic performance concerns ensuring an economic gain for each energy user. In addition, the environmental performance was assessed for each scenario, by defining the annual GHG emissions (tCO2eq) for the annual consumption of electricity withdrawn from the national grid [12] to define the optimal intervention that guarantees the energy supply to the greatest number of users at the lowest economic and environmental cost. To assess cost-optimal analysis, the Global Cost approach (Standard EN 15459:2007), has been applied to each scenario. The Global Cost consists of calculating the present value of the Energy Costs, referring to the initial year, including the initial Investment Cost, as it is expressed in Eq. (8),

$C_{G}(\tau)=C_{I}+\sum_{i=l}^{\tau}\left(C_{E i} \cdot R_{d}(i)\right)$     (8)

where, $C_{G}$ is the Global Cost referred to the initial year $\tau_{0} ; C_{I}$ is the initial Investment Cost; $C_{E, i}$ the annual Energy Cost at year i; $R_{d}$ the discount factor at year i, equals to $2 \%$.

Table 4. Investment costs

The Investment Cost refers to the expenses incurred to implement different interventions. Table 4 shows the marginal costs for each type of intervention: for the photovoltaic (PV) and storage (ST) installation, different costs have been identified, considering the size of the plant (https://www.qualenergia.it); lamp replacement costs refer to [13], and costs for the REC establishment refers to the annual contribution envisaged by the Piedmont Region (R.L. 12/2018), supporting technical and legal expenses.

The annual Energy Costs are calculated with Eq. (9); it considers the aggregation of all the expenses for the withdrawal of energy incurred by users, and the aggregation of all the revenues generated by the sale profit of the energy fed into the grid; revenues also include the savings from the Self-Consumption (SC) and Collective Self-Consumption (CSC), as they correspond to a lack of expenditure. To the CSC is related the profit (in €/year) from the incentive as envisaged by the Italian law (MD Sept. 16,2020).

Annual Energy Cost $C_{E}=\left(\Sigma\right.$ Expenses- $\sum$ Revenues $)$    (9)

Each energy flows of paragraph 4.4 are associated with a different energy price, depending on the direction of the flow (withdrawal or sale), the electricity grid (national or local), and the category of end user (company, municipality, residential), as it synthesized in Table 5. The REC incentive refers to the REC configuration as a single entity, it is applied to the total energy exchanges between members (Collective Self-Consumption), without distinction by category of end user; its value is fixed for the duration of the incentive itself. The energy prices on the national grid (NG) refer to the real energy market prices in the area of the case study: the withdrawal energy prices (ENG) corresponds to the average price in the bill by end user category, provided by ACEA Pinerolese SpA. The energy price for the energy sale to the national grid (RNG) corresponds to the hourly price of the year 2017 for the north-west area of the Italian electricity market, as provided by GSE (http://www.fattoriedelsole.org/servizi/Documenti%20Privati/Prezzi%20medi%20RID%20(aggiornato%2012-17).pdf).

The annual Expenses and Revenues (in €/year) have been calculated according to Eq. (10) and (11), respectively.

Expenses $=(U D+C S C) E_{N G}$      (10)

Revenues $=\left(O P^{*} R_{N G}\right)+\left(C S C_{R E C_{\text {in }}}\right)+\left(S C \cdot E_{N G}\right)$     (11)

Table 5. Energy prices [€/kWh]

4.7 Scenarios

In the first two scenarios interventions are supported by each stakeholder independently; from the third scenario onwards, the REC establishment is associated with different interventions which consider the inclusion of additional members, storage system installation, energy efficiency measure or a combination of them. These scenarios are:

1. Singular POD. Each Point of Delivery associated to a building or a service is separately analyzed; the municipal user (MUN) is the only user that owns more than one POD. Both residential (SH) and company (CR) users are recipients of a singular POD, therefore scenarios 1) and 2) coincide.
2. Singular prosumer. Municipality (MUN) user, aggregates energy consumption and productivity of the two municipal buildings and the public lighting.
3. REC scenario. All stakeholders participate with their PODs becoming members of the REC.
4. REC+50Ru. Starting from scenario 3), the number of end users is optimized to reach the maximum level of annual SCI. It is assumed to include 50 typical residential users that are exclusively “consumers” and reside in the municipality.
5. REC+ST. Starting from scenario 3), it is assumed to integrate each new PV system of the REC with a storage system (ST) to optimize the Over Production, shifting through time the Collective Self-Consumption of the different REC members that have an evening Uncovered Demand.
6. REC+LED+ST. Starting from scenario 3), it assumes to replace all the HPS lamps of the municipal public lighting service with new LED light sources to reduce the energy consumption of about the 37%, as described in [13]. Based on the new municipal consumption, the capacity of the storage system to be installed for this user is update; storage systems are also assumed for all other users and for the REC.
7. REC+LED+ST+12Ru. Starting from scenario 6), the number of end users joining the REC is optimized to reach the maximum level of annual SCI. As scenario 4), it is assumed to include 12 typical residential consumers.

The methodology explained in this work is useful to measure RECs in terms of energy security and economic convenience. Energy planning is fundamental to guarantee good results, as the “energy vocation” of the territory. As in this case study, mountain areas have great RES availability and low consumption; therefore, it is easier to reach high values of self-consumption and self-sufficiency.

The analysis carried out show how to increase the energy self-sufficiency, optimizing self-consumption by aggregating different users, in combination with energy efficiency interventions and storage systems. Batteries can lead to better results, but they have high environmental and economic costs.

It is also evident the importance of economic incentives that promote end user initiatives in implementing energy efficiency measures in a coordinated and cooperative manner. Through the place-based methodology it is possible to highlight the opportunities generated by the different aggregations of end users in a specific territory, comparing different intervention scenarios to identify the one that can ensure economic benefits for all stakeholders. In Italy, there are existing available economic incentives aim at supporting investment costs. Municipal users can access to the Law Decree “Crescita” (L.D. 34/2019, art. 30) that make available € 100,000 for energy efficiency improvement of public buildings. Company users have access to the Law Decree “Milleproroghe” (DL 162/2019) which allows a tax deduction of 50 % up to a total amount of € 96,000 per property unit. Residential users can link the PV installation to the “Superbonus 110 %” economic incentive, as established by law (N.L. 77/2020), as a subordinate intervention to the energy retrofit of the building.

The importance of incentives can be translated into the importance of local energy policies aimed at supporting such initiatives. An important phase of the work is to inform citizens through participatory operations campaign.

The results obtained in this work can provide technical support to policy makers. The applied methodology can be replicated and adapted to other contexts such as the case study of the Pinerolo Energy Community. In future works, other intervention scenarios could be hypothesized such as the energy retrofit of buildings, the energy demand optimization through real-time monitoring systems or the exploitation of other available local RES, such as hydroelectric or biomass.