Economic assessment of self-consumption and energy communities: Profit distribution insights from a real case study

Contents lists available at ScienceDirect Energy Conversion and Management: X journal homepage: www.sciencedirect.com/journal/energy-conversion-and-management-x Economic assessment of self-consumption and energy communities: Profit distribution insights from a real case study Jose Miguel Riquelme-Domingueza,∗, María Emilia Sempértegui a, Juan Manuel Roldan-Fernandeza, Javier Serrano-Gonzaleza, Jesus Manuel Riquelme-Santosa,b aElectrical Engineering Department, Escuela Técnica Superior de Ingeniería, Universidad de Sevilla, Camino de los Descubrimientos, Seville, 41092, Spain bENGREEN Laboratory of Engineering for Energy and Environmental Sustainability, University of Seville, Seville, Spain A R T I C L E I N F O Keywords: Energy community Profit Self-consumption Sharing approaches A B S T R A C T The deployment of solar energy for self-consumption provides an opportunity to restructure energy systems by harnessing energy and allowing individuals to actively participate in the energy transition, resulting in more significant profits. This work compares the photovoltaic (PV) electricity production for residential prosumers under three scenarios, in which: (1) the PV systems are designed to supply the individual demands of each user optimally; (2) with the exact PV capacity of the first scenario, the users decide to form an energy community; and (3), the prosumers decide to consolidate as an energy community from the beginning, and the whole PV system is designed to cover the demand of all the users optimally. Results show that energy communities in general, and creating the community from zero in particular, are more cost-effective than when the prosumers invest and manage their own PV system individually. The paper also discusses the distribution of the additional profits considering four allocation strategies, with the sharing approach based on the optimal individual photovoltaic power capacity being the most advantageous for all prosumers of the community. Specifically, with this sharing strategy, all prosumers reduce their payback, all prosumers increase the Net Present Value of their investment, and all prosumers pay less than 50% of what they pay when they do not have a self-consumption installation. 1. Introduction The steady increase in electricity prices in recent years, in addition to the promotion of energy from renewable sources, has generated a growing interest and desire to adopt photovoltaic (PV) electricity generation systems in homes [1]. The main objective of this trend is to harness the available solar radiation for local electricity production, which in turn leads to a significant reduction in electricity billing prices [2,3]. However, in some countries, there is an innovative trend that goes beyond the simple adoption of solar panels in individual homes. This trend called energy communities represents a collective and collaborative approach to electricity generation and management, as a group of neighbours or citizens who come together to generate, consume, store, and even sell the electricity they produce from renewable energy sources [4]. Although there are other energy carriers such as thermal energy or hydrogen [2], this study focuses exclusively on energy communities in which electrical energy from distributed photovoltaic systems is exchanged. ∗Corresponding author. E-mail address: [email protected] (J.M. Riquelme-Dominguez). Energy communities gain energy independence, achieve social, environmental and economic benefits, and promise to play a crucial role in the future of electricity production and use locally and globally [5]. 1.1. Background 1.1.1. Energy communities in Europe At the European level, energy communities have obtained legal recognition in the ‘‘Clean Energy for all Europeans package’’ in Directive (EU) 2018/2001 of the European Parliament and of the Council on the promotion of the use of energy from renewable sources, and in Directive (EU) 2019/944 of the European Parliament and the Council concerning common rules for the internal market in electricity [6,7]. Energy communities are recognised in the regulations of some countries such as Germany, Denmark, Poland, the United Kingdom, France, https://doi.org/10.1016/j.ecmx.2025.101198 Received 21 March 2025; Received in revised form 1 June 2025; Accepted 11 August 2025 Energy Conversion and Management: X 28 (2025) 101198 Available online 20 August 2025 2590-1745/© 2025 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/bync-nd/4.0/ ). J.M. Riquelme-Dominguez et al. Nomenclature Acronyms AC Aggregated Community BOS Balance of System CZ Community from Zero ED Equitable Distribution EU European Union e⋅ sios Sistema de Información del Operador del Sistema [21] IRR Internal Rate of Return ISC Individual Self-Consumption MPPT Maximum Power Point Tracking NOCT Nominal Operating Cell Temperature NPV Net Present Value PC Power Capacity PV Photovoltaic SS Sunshine STC Standard Test Conditions TC Total Consumption Subscripts and superscripts ℎSubscript referring to the hth hour 𝑖Subscript referring to the 𝑖th prosumer 𝑗Subscript referring to the 𝑗th prosumer 𝑚Subscript referring to the 𝑚th month 𝑛𝑜𝑃 𝑉 Superscript referring to a situation in which no photovoltaic installation is considered 𝑃 𝑉 Superscript referring to a situation in which photovoltaic installations are considered 𝑡Subscript referring to the 𝑡th year Symbols 𝛼Power temperature coefficient [%∕°C] 𝛽𝑖Sharing coefficient of the 𝑖th prosumer [pu] 𝜂Efficiency of the photovoltaic installation [%] 𝜂𝑎𝑐 Efficiency factor representing AC wiring losses [%] 𝜂𝑑𝑐 Efficiency factor representing DC wiring losses [%] 𝜂𝑖𝑛𝑣 Inverter efficiency [%] 𝜂𝑚𝑝𝑝𝑡 Efficiency factor representing losses due to the non-tracking of the maximum power point [%] 𝜂𝑜𝑢𝑡𝑎𝑔𝑒 Efficiency factor representing system availability due to outages or maintenance [%] 𝜂𝑝𝑣 Efficiency of the PV module [%] 𝐶𝑏 ℎ Hourly balancing costs [e] 𝐶𝐹 𝑖,𝑚 Fixed costs of the energy bill for the 𝑖th prosumer in the month m. It corresponds to the non-clearable part of the invoice [e] 𝐶𝑇 𝑖,𝑚 Total cost of the energy bill for the 𝑖th prosumer in the month m [e] 𝐶𝑉 𝑖,𝑚 Variable costs of the energy bill for the 𝑖th prosumer in the month m. It corresponds to the compensable part of the invoice [e] 𝐶𝐹𝑖,𝑡 Cash flow of the 𝑖th prosumer in the 𝑡th year [e] 𝐸𝐿 𝑖,ℎ Individual energy consumed by the 𝑖th prosumer in the hour h [kWh] 𝐸𝑃 ,𝐶𝑂𝑀 𝑖,ℎ Energy that the 𝑖th prosumer purchases from the community in the hour h [kWh] 𝐸𝑃 ,𝐺𝑅𝐼𝐷 𝑖,ℎ Energy that the 𝑖th prosumer purchases from the grid in the hour h [kWh] 𝐸𝑃 𝑉 𝑖,ℎ Individual energy generated by the 𝑖th prosumer in the hour h [kWh] 𝐸𝑃 𝑉 ,𝐶𝑂𝑀 ℎ Collective energy generated by the energy community in the hour h [kWh] 𝐸𝑆,𝐶𝑂𝑀 𝑖,ℎ Energy that the 𝑖th prosumer sells to the community in the hour h [kWh] 𝐸𝑆,𝐺𝑅𝐼𝐷 𝑖,ℎ Energy that the 𝑖th prosumer sells to the grid in the hour h [kWh] 𝐸𝑆𝐶 𝑖,ℎ Self-consumed energy by the 𝑖th prosumer in the hour h [kWh] 𝐼𝑖𝑛𝑣,𝑖 Initial investment of the 𝑖th prosumer [e] 𝑘Interest rate [%] 𝑙𝑠 Life span of the installation [y] 𝑛𝑝𝑣 𝑖 Number of PV modules installed by Prosumer i 𝑛𝑝 Number of prosumers 𝑂𝑝𝐸𝑥𝑖,𝑡 Maintenance and operational expenditures of the 𝑖th prosumer in the 𝑡th year [e] 𝑃𝐶 𝑖,𝑚 Power contracted by the 𝑖th prosumer in the month m [kW] 𝑃𝑚 ℎ Hourly spot market prices [e] 𝑃𝑝𝑣 Rated power of a single PV module under STC [W] 𝑃𝑇 𝑚 Power term in the month m [e/kW] 𝑃 𝑆𝐻 ℎPeak Sun Hours for hour h [h] 𝑃 𝑉 𝑃 𝐶ℎHourly price of the regulated tariff for domestic prosumers [e] and Italy [8]. At the same time, in other countries, they are expected to do so in the short term. This legislative scope is reflected in the fact that there are currently around 4848 energy communities in Germany, 987 in the Netherlands, 633 in Denmark and 235 in Spain [9,10]. 1.1.2. Energy communities in Spain The first step towards the establishment of energy communities is set out by Royal Decree 244/2019 [11], which regulates collective self-consumption, which defines the conditions under which prosumers can generate their electricity and use it for self-consumption, also allowing compensation of surplus energy in the electricity grid. The main objective of Royal Decree 244/2019 is to encourage and regulate individual and collective self-consumption of renewable energy as a way of promoting energy efficiency and the transition towards cleaner and more sustainable energy sources. In particular, Article 4 of Royal Decree 244/2019 defines the two categories of self-consumption available in Spain: •Self-consumption without energy surplus: for those installations equipped with an anti-spill device preventing surplus energy from flowing into the grid. Energy Conversion and Management: X 28 (2025) 101198 2 J.M. Riquelme-Dominguez et al. •Self-consumption with energy surplus: for those installations that, in addition to supplying energy for self-consumption, can inject energy into the grid: –Under compensation: The prosumer receives a discount on their bill for the surplus energy injected into the grid. –Without compensation: The prosumer cannot receive a discount because they did not meet certain requirements to be eligible for this subcategory. For the case in which the prosumers can reduce their bill at the end of the month, there are two alternatives: simplified compensation for individual self-consumption and compensation for collective self-consumption. The simplified compensation mechanism evaluates the deficit between consumption and the surplus of the generation facilities by employing a balance in economic terms of the surplus energy injected into the grid. The economic value of the surplus hourly energy cannot be greater than the monetary value of the hourly energy consumed in the monthly billing period. In the case of collective selfconsumption, each month, the distribution company reviews the net hourly generation meter of a self-consumption installation. It provides the necessary information to the retailer to carry out the billing and compensation of surplus energy to each prosumer individually. In the latter case, all prosumers must have distribution coefficients for the correct distribution of the additional benefits generated. These coefficients may be defined by the power to be invoiced, the economic contribution to the generation facility, or any criterion that all participants agree on. 1.2. Literature review Some previous work dealt with energy communities and how to allocate investment, maintenance costs, and benefits among their participants. The study in [12] analyses the impact of different investment options in combination with cost sharing for energy communities. This study considers two investment options: through a third-party or community members’ investment and three modes of cost sharing. However, this study lacks a detailed analysis of benefit sharing for the energy community. The paper [13] studies the different tariffs available in Australia for an efficient, effective, and fair distribution of the costs and benefits of self-consumption of embedded networks. However, this study does not consider the optimal design of PV installations at the individual or collective level. For example, suppose that one of the participants has a higher consumption profile in the central hours of the day. In that case, this prosumer should install more PV power than another prosumer who mainly consumes at night. In this study, the photovoltaic system of the energy community of 72 dwellings is dimensioned globally, considering only 75, 110, and 145 kW of installed power. The work [14] proposes a new heuristic sharing mechanism proportional to the marginal contribution of each participant to the energy community. This method aims to be fair to all members of the community (fairness being understood as when the sharing is a faithful representation of each participant’s contribution) and to reduce the computational cost of the sharing algorithm. However, the paper concludes that fair sharing is more complicated when the energy community members have different consumption profiles and that even the method is only valid when the users are similar. If one prosumer stands out above the rest, sharing benefits could be unfair. Therefore, the success of the model depends to a large extent on the homogeneity of the members of the energy community. In [15], the performance of an energy community of 100 dwellings is analysed when its photovoltaic installation has a power of 100 kWp. Although the study considers six different scenarios and different categories of prosumers depending on their consumption profile, the study results are as expected. If there is a uniform distribution of profits, prosumers with a low consumption profile who are not synchronised with the hours of sunshine will benefit unfairly. If, otherwise, the individual contribution of each home is considered, those with a higher percentage of consumption during sunshine hours are the ones that benefit the most. The article [16] investigates the current mechanisms used in Spain to allocate energy self-consumed by the different members of the energy community, as well as the economic rights over surplus energy. This work shows that the energy communities in Spain are not yet fully exploited and proposes a method of ex post distribution. However, this paper does not analyse the problem from the point of view of the individual prosumer, which may lead to unfair sharing approaches. The work [17] implements the Hawk-Dove evolutionary game model to incentivate social cooperation between prosumers. This strategy satisfactorily seeks for the altruism of the prosumers, but only equal distribution of benefits is applied among the prosumers when they select the Dove behaviour. The study [18] supports the management of energy communities by defining four new dynamic sharing allocation criteria. The term dynamic refers to the fact that the sharing coefficients vary from hour to hour. Although the paper highlights the strengths and limitations of the methods, the results show that some of the users have a greater amount of shared energy than their real sharing potential, which is unfair. This work reflects the need to look for more and more fair sharing allocation mechanisms within energy communities. 1.3. Contributions A review of the literature reveals that prior studies typically begin with the assumption that the energy community is already established. However, prosumers with self-consumption photovoltaic installations have the potential to come together and create an energy community. This introduces a novel perspective that enables the exploration of innovative methods to share surplus energy (or even costs) and assess which approaches are the most just. The objective of this work is to make an economic study of the viability of two modalities of energy communities compared to individual self-consumption. Three PV deployment scenarios are considered, in which: (i) individual PV systems are optimised for each user’s demand (Individual Self-Consumption, ISC), (ii) the formation of the energy community is done using pre-installed PV systems (Aggregated Community, AC), and (iii) the energy community is created and the PV system is optimally designed from the outset (Community from Zero, CZ). In the AC case, only the incremental profit is distributed among the community members. In the CZ case, the total investment, the total operating and maintenance costs, and the total profits are distributed according to the sharing coefficients. The analysis is carried out both from a global point of view and at the level of the individual prosumer. Finally, there is a debate on which sharing allocation strategy is more fair. This paper makes the following key contributions: 1. A comprehensive comparison of PV electricity production for residential prosumers under three distinct scenarios. The proposed methodology is applicable to any set of prosumers, as long as their consumption profiles and solar exposure of their location are taken into account. 2. Demonstration that energy communities, particularly those formed from the beginning with coordinated PV system design, are significantly more cost-effective compared to individual PV investments and management. 3. Analysis of four sharing allocation strategies for energy communities, identifying the optimal sharing approach based on the individual PV capacity. This strategy showed, in the case analysed: to reduce payback periods for all community members, to increase the Net Present Value of investments for all members, and to lower energy costs for all participants by more 50% compared to the absence of self-consumption installations. Energy Conversion and Management: X 28 (2025) 101198 3 J.M. Riquelme-Dominguez et al. Fig. 1. View of the dwellings under study. The remainder of the paper is organised as follows. Section 2 introduces the proposed methodology, Section 3 presents the numerical results, and in Section 4 a discussion on different sharing coefficients for energy communities is provided. Finally, Section 5 draws the conclusions of the paper. 2. Methodology The methodology used for the evaluation of individual selfconsumption, the aggregated energy community, and the energy community from zero is presented employing a real case study. The study is applied to 𝑛𝑝 residential prosumers, where the benefits of photovoltaic installations for individual self-consumption are analysed first and later when the group of prosumers decides to configure itself as an energy community. For this purpose, the sizing of the photovoltaic system for self-consumption as well as for the energy community is carried out based on real data on electric energy demand with hourly discretisation in one year, temperature, irradiation, billing price for energy consumed, and the price of surplus energy for self-consumption considering the simplified compensation mechanism. 2.1. Location The dwellings under study are located in Burguillos, Seville, Spain. The latitude of the emplacement is 37.3807◦, and the longitude is −6.0682◦. The altitude above sea level is 123 m. The descriptive data for the dwellings have been obtained from the website of Cadastral Electronic Site [19], where the constructed surface of each dwelling is between 116 and 118 m2, and the roof area for the location of the photovoltaic modules varies between 31.95 m2 (7.33 m × 4.36 m) and 35.40 m2 (8.12 m × 4.36 m). It is important to note that the dwellings are single-family houses, with roofs for individual use. Fig. 1 shows a satellite view of the homes in the study. 2.2. Energy consumption profiles The proposed methodology considers the energy demand profiles of the residential users described and the collective consumption profile as the total of the individual demands of the prosumers. Table 1 shows the average monthly demand for each prosumer in kilowatts per hour. Individual demands range from 112.5 to 399.0 kWh, reflecting a large variability in individual consumption profiles. Although the reasons why prosumers have different consumption profiles are unknown and fall outside the scope of this study, they could be due to the type of household (e.g., working professionals, retirees, families with school-aged children), occupancy schedules, the presence of specific energy-intensive appliances, and potentially socio-economic factors influencing energy usage patterns. Hereafter, the authors will treat this variability as simply consumption habits. Fig. 2 shows the average hourly consumption profiles over the whole year (including weekdays and holidays) for Prosumers 2, 4, 6, Table 1 Individual average monthly demand. Prosumer Avg. monthly Prosumer Avg. monthly demand (kWh) demand (kWh) 1 192.851 11 198.975 2 358.622 12 131.196 3 244.046 13 118.086 4 762.445 14 234.260 5 379.123 15 399.045 6 273.855 16 175.500 7 313.088 17 358.854 8 112.484 18 241.969 9 302.805 19 238.073 10 229.852 20 217.999 Fig. 2. Average hourly consumption profiles over the whole year (including weekdays and holidays) for Prosumers 2, 4, 6, 8, and 10. 8, and 10, expressed in kilowatts. This averaging approach aims to capture the general consumption patterns while smoothing out daily and seasonal variations for the purpose of the economic assessment. The solid blue line in Fig. 2 represents the average hourly consumption, while the blue shaded area shows the standard deviation of these values. The variation in prosumers’ energy consumption throughout the day can be appreciated, where it can be seen that some prosumers used to reach maximum energy demand after 18:00 (Prosumers 2 and 10) and others in the mornings (Prosumers 4, 6, and 8), depending on each user’s consumption habits. Similar to the average individual consumption profiles, Fig. 3 represents the average hourly consumption profile for everyday in the year (solid blue line) and its standard deviation (blue shaded area) of all prosumers as a whole, that is, considering collective consumption. The maximum energy consumption of the aggregate demand used to occur at 19:00 h, coincident with the time when prosumers returns from work. 2.3. Irradiance and temperature data The hourly irradiance and temperature data over one year have been obtained from the PVGIS website [20] by entering the location Energy Conversion and Management: X 28 (2025) 101198 4 J.M. Riquelme-Dominguez et al. Fig. 3. Average hourly consumption profile over the whole year (including weekdays and holidays) for all prosumers: collective consumption. Table 2 Average monthly solar irradiance. Month Horizontal 15◦ tilt Avg. plane irradiance plane irradiance Temp. (◦C) (kWh/m2/day) (kWh/m2/day) Jan 2.946 5.200 10.1 Feb 4.258 6.518 12.5 Mar 5.399 6.797 15.9 Apr 5.986 6.330 16.6 May 7.662 7.261 23.4 Jun 8.241 7.435 23.9 Jul 7.660 7.095 26.5 Aug 7.268 7.421 28.2 Sep 5.571 6.539 24.9 Oct 4.344 5.995 20.4 Nov 2.664 4.113 14.3 Dec 2.442 4.353 12.5 Table 3 Average monthly energy prices. Month Avg. Consumed Avg. Surplus energy price energy price (e/MWh) (e/MWh) Jan 285.86 201.38 Feb 286.31 199.83 Mar 387.60 282.85 Apr 268.61 191.06 May 259.95 186.70 Jun 290.94 169.14 Jul 331.38 142.30 Aug 395.60 154.50 Sep 315.86 140.37 Oct 226.65 126.57 Nov 189.44 114.84 Dec 206.45 96.15 data of the dwellings. Table 2 presents the average monthly solar irradiance and temperature data at the locations of the houses. The angle of tilt of the PV modules has been defined according to local regulation that does not allow support structures to be placed on sloping roofs. In other words, the tilt has to be the same as slope of the roof, which is 15◦. Regarding the azimuth angle, a uniform value of 0◦ (south-facing) has been assumed for all photovoltaic modules. This simplifies the analysis and aligns with the predominant orientation of the dwellings. Although the real azimuths vary slightly (as shown in Fig. 1), the associated annual variation in irradiance is minor (below 4%), and its effect is already partially captured by the inclusion of a Maximum Power Point Tracking (MPPT) efficiency factor in the PV production model. Table 4 Main features of the 400 W PV Module at STC (1000 W/m2 and 25◦). Weight (kg) 22.5 Dimensions (mm) 2000 × 991x40 Number of cells 144 (6 × 24) Power (W) 400 Open-circuit voltage (V) 49.17 Maximum power voltage (V) 40.92 Short-circuit current (A) 10.34 Maximum power current (A) 9.78 Efficiency (%) 20.2 2.4. Billing price for energy consumed and compensation of surplus energy The economic evaluation of this study requires hourly data on the billing cost of the energy consumed and the price at which the surplus energy injected into the grid is compensated. This information is provided by the Spanish Transmission System Operator, Red Eléctrica de España, through its system operation information system, e⋅ sios [21]. Table 3 shows the average monthly prices of the energy consumed and the surplus energy in euros per megawatt hour. As detailed in Table 3, the price at which the surplus energy is compensated is lower than purchasing energy from the retailer. 2.5. Components of the photovoltaic system This work considers a set of commercial components of photovoltaic systems to obtain a reference budget. For some elements of the installation, such as the inverter, the selection of a single-phase or a three-phase inverter affects the inverter cost and the power it can inject. 2.5.1. Solar panels The size of the photovoltaic systems was carried out using 400 W monocrystalline silicon solar panels [22]. The main characteristics of the photovoltaic module in Standard Test Conditions (STC) are listed in Table 4. The photovoltaic panels to be installed in the homes will be of the same power, model, and manufacturer. 2.5.2. Single-phase inverters For the sizing of photovoltaic panels for individual self-consumption, single-phase inverters of varying power ratings [23] have been chosen because the peak power of the photovoltaic installation varies according to the electricity consumption of each prosumer. These inverters have the following built-in protections: anti-islanding protection, residual current monitoring unit, and overcurrent and overvoltage protection. 2.5.3. Three-phase inverter For the case where prosumers are configured as an energy community, three-phase inverters of different power ratings [24] have been chosen. These inverters have the following built-in protections: disconnection device on the input side, anti-islanding protection, arc fault protection, and overcurrent and overvoltage protection. 2.6. Design of photovoltaic systems for the individual self-consumption The methodology for the optimal design of individual self-consumption installations is as follows. The starting point is to obtain the hourly ambient temperature (𝑇𝑎𝑚𝑏 ℎ) and the hourly irradiance (𝐺ℎ) at the location of the dwellings using the PVGIS tool (Section 2.3). Then, the hourly PV energy produced by each installation (𝐸𝑃 𝑉 𝑖,ℎ) is calculated using the following expression, which incorporates the module parameters, temperature effects, and system losses (Balance of System, BOS): Energy Conversion and Management: X 28 (2025) 101198 5 J.M. Riquelme-Dominguez et al. Table 5 Investment options for Prosumer 1. Number of 1 2 3 4 6 7 8 9 10 11 PV modules 5 Annual PV 677.49 1354.98 2034.56 2718.32 4081.66 4761.94 5453.37 6135.04 6816.71 7498.38 generation (kWh) 3397.90 Annual self-consumed 320.21 488.91 626.67 741.42 909.19 969.11 1020.08 1062.31 1097.36 1126.88 energy (kWh) 834.76 Annual 44.31 124.52 197.90 266.99 292.20 264.61 204.36 166.75 114.33 47.11 savings (e)300.15 Investment (e) 1077.42 1556.57 2072.42 2615.60 3094.75 3664.84 4214.55 4975.60 5465.38 6041.77 6780.82 NPV (e) 997.64 2481.05 3855.22 5161.55 5836.31 5824.82 5471.15 4602.73 4063.80 3289.32 2296.27 Payback (years) 10.73 7.63 6.83 6.56 6.77 7.64 8.74 10.73 12.09 14.01 16.76 𝐸𝑃 𝑉 𝑖,ℎ =𝑛𝑝𝑣 𝑖 ⋅𝑃𝑝𝑣 ⋅𝑃 𝑆𝐻 ℎ⋅ ⋅(𝜂𝑝𝑣 −𝛼⋅(𝑇𝑎𝑚𝑏 ℎ +𝐺ℎ⋅(𝑁𝑂𝐶𝑇 − 20) 800 − 25))⋅ ⋅𝜂𝑑𝑐 ⋅𝜂𝑎𝑐 ⋅𝜂𝑜𝑢𝑡𝑎𝑔𝑒 ⋅𝜂𝑚𝑝𝑝𝑡 ⋅𝜂𝑖𝑛𝑣 (1) where: 𝑛𝑝𝑣 𝑖 is the number of PV modules installed by Prosumer i; 𝑃𝑝𝑣 is the rated power of a single PV module under STC; 𝑃 𝑆𝐻 ℎ is the Peak Sun Hours for hour h; 𝜂𝑝𝑣 is the efficiency of the PV module, a parameter that takes into account parameter dispersion between modules (2%), dust and dirt effect (1%), angular and spectral reflectance losses (2%), and shading factor (1%); 𝛼 is the power temperature coefficient, quantifying how performance decreases with increased cell temperature; NOCT is the Nominal Operating Cell Temperature of the PV module; 𝜂𝑑𝑐 is the efficiency factor representing DC wiring losses (1.5%); 𝜂𝑎𝑐 is the efficiency factor representing AC wiring losses (0.5%); 𝜂𝑜𝑢𝑡𝑎𝑔𝑒 is the efficiency factor representing system availability due to outages or maintenance (5%); 𝜂𝑚𝑝𝑝𝑡 is the efficiency factor representing losses due to the non-tracking of the maximum power point due to the installation of fixed mounting structures (5%); and 𝜂𝑖𝑛𝑣 is the inverter efficiency. These parameters and their effects are part of the BOS considered in the hourly PV energy calculation. Using this estimated hourly production (𝐸𝑃 𝑉 𝑖,ℎ) and the hourly energy demand of each consumer (𝐸𝐿 𝑖,ℎ), the hourly self-consumed energy by each consumer (𝐸𝑆𝐶 𝑖,ℎ) is determined, as shown in Eq. (2): 𝐸𝑆𝐶 𝑖,ℎ ={𝐸𝑃 𝑉 𝑖,ℎ 𝑖𝑓 𝐸𝑃 𝑉 𝑖,ℎ ≤𝐸𝐿 𝑖,ℎ 𝐸𝐿 𝑖,ℎ 𝑖𝑓 𝐸𝑃 𝑉 𝑖,ℎ > 𝐸𝐿 𝑖,ℎ (2) From the self-consumed hourly energy, it is feasible to determine the energy that each prosumer purchases from the grid in each hour (𝐸𝑃 ,𝐺𝑅𝐼𝐷 𝑖,ℎ) and the individual surplus hourly energy (i.e., hourly energy sold to the grid 𝐸𝑆,𝐺𝑅𝐼𝐷 𝑖,ℎ) by means of Eqs. (3) and (4): 𝐸𝑃 ,𝐺𝑅𝐼𝐷 𝑖,ℎ =𝐸𝐿 𝑖,ℎ −𝐸𝑆𝐶 𝑖,ℎ (3) 𝐸𝑆,𝐺𝑅𝐼𝐷 𝑖,ℎ =𝐸𝑃 𝑉 𝑖,ℎ −𝐸𝑆𝐶 𝑖,ℎ (4) Knowing 𝐸𝑃 ,𝐺𝑅𝐼𝐷 𝑖,ℎ and 𝐸𝑆,𝐺𝑅𝐼𝐷 𝑖,ℎ, the monthly variable term of each prosumer’s energy bill (𝐶𝑉 𝑖,𝑚) is calculated, taking into account the hourly price of the regulated tariff (𝑃 𝑉 𝑃 𝐶ℎ), the hourly spot market price (𝑃𝑚 ℎ) and the hourly compensation costs (𝐶𝑏 ℎ). It is important to note that, according to the regulations, the variable term of the bill can never be negative, as reflected in Eq. (5): 𝐶𝑉 𝑖,𝑚 =∑ ℎ=1 𝐸𝑃 ,𝐺𝑅𝐼𝐷 𝑖,ℎ ⋅𝑃 𝑉 𝑃 𝐶ℎ−∑ ℎ=1 𝐸𝑆,𝐺𝑅𝐼𝐷 𝑖,ℎ ⋅(𝑃𝑚 ℎ −𝐶𝑏 ℎ) ≥0 ∀𝑖, 𝑚 (5) Variable costs are highly dependent on the self-consumption installation of photovoltaic energy. In this paper, two variable cost options assess the effect of self-consumption installations: (i) when the prosumer has a photovoltaic installation (𝐶𝑃 𝑉 𝑉 𝑖,𝑚, being in general, 𝐸𝑆 𝑖,ℎ ≠ 0) and (ii) when the prosumer does not have a self-consumption installation (𝐶𝑛𝑜𝑃 𝑉 𝑉 𝑖,𝑚 , being necessary 𝐸𝑆 𝑖,ℎ = 0 ∀ℎ). Concerning the fixed costs of the electricity bill, these depend on the power contracted by the prosumer i during month m (𝑃𝐶 𝑖,𝑚) and the power term in that month (𝑃𝑇 𝑚): 𝐶𝐹 𝑖,𝑚 =𝑃𝐶 𝑖,𝑚 ⋅𝑃𝑇 𝑚 (6) The total cost consists of the fixed term and the variable term of the bill according to Eqs. (7) and (8), depending on whether the user has a self-consumption installation. In both cases, the fixed term of the bill is identical. 𝐶𝑛𝑜𝑃 𝑉 𝑇 𝑖,𝑚 =𝐶𝐹 𝑖,𝑚 +𝐶𝑛𝑜𝑃 𝑉 𝑉 𝑖,𝑚 (7) 𝐶𝑃 𝑉 𝑇 𝑖,𝑚 =𝐶𝐹 𝑖,𝑚 +𝐶𝑃 𝑉 𝑉 𝑖,𝑚 (8) An economic analysis has been performed to determine the optimal number of photovoltaic panels to be installed by each prosumer according to their consumption habits. For this purpose, the energy prices of the prosumers when they do not have self-consumption installations are compared with the fees they incur when they install PV panels in their homes. The annual economic savings are determined by Eq. (9), where 𝐶𝐹𝑖,𝑡 are the savings of the ith prosumer’s bill in the year t, and 𝑂𝑝𝐸𝑥𝑖,𝑡 are the operating and maintenance costs of that prosumer’s PV installation in that year. 𝐶𝐹𝑖,𝑡 = 12 ∑ 𝑚=1(𝐶𝑛𝑜𝑃 𝑉 𝑇 𝑖,𝑚 −𝐶𝑃 𝑉 𝑇 𝑖,𝑚)−𝑂𝑝𝐸𝑥𝑖,𝑡 (9) Taking into account the initial investment 𝐼𝑖𝑛𝑣,𝑖, the lifetime of the PV installation 𝑙𝑠, and the interest rate 𝑘, it is possible to determine the Net Present Value (𝑁𝑃 𝑉𝑖) and the Internal Rate of Return (𝐼𝑅𝑅𝑖) of the various investment options for the prosumer i through Eqs. (10) and (11) [25]: 𝑁𝑃 𝑉𝑖= −𝐼𝑖𝑛𝑣,𝑖 + 𝑙𝑠 ∑ 𝑡=1 𝐶𝐹𝑖,𝑡 (1 + 𝑘)𝑡(10) 0 = −𝐼𝑖𝑛𝑣,𝑖 + 𝑙𝑠 ∑ 𝑡=1 𝐶𝐹𝑖,𝑡 (1 + 𝐼𝑅𝑅𝑖)𝑡(11) The NPV calculation takes into account a life span of the solar panels of 25 years (𝑙𝑠 = 25 y) and assuming an interest rate of 𝑘= 5%. As an example of the NPV calculation for Prosumer 1, Table 5 shows annual PV generation, annual self-consumed energy, annual savings of installing a different number of photovoltaic modules, the investment required, the NPV, and the payback time of each investment option. The possibility for each individual prosumer to install solar panels from 1 to 11 was determined considering the available roof area per dwelling. Table 5 reflects that PV generation increases proportionally as the number of PV panels increases, while self-consumed energy does not. This is because not all additional PV generation is aligned with the consumption hours of Prosumer 1. Based on the data in Table 5, the PV system consisting of 5 solar panels (2 kW) is selected for Prosumer 1, as the NPV is higher with this number of panels. In this case, installing five PV modules is more profitable than installing any other amount of panels, as it requires a moderate investment and early payback of the investment. Following this example, the number of panels required Energy Conversion and Management: X 28 (2025) 101198 6 J.M. Riquelme-Dominguez et al. Table 6 Photovoltaic systems sizes for individual self-consumption. Prosumer Nb. of PV PV power Inverter 𝜂 (%) modules (kW) power (kW) 1 5 2.0 2.0 24.57 2 10 4.0 4.2 23.25 3 7 2.8 3.0 21.24 4 11 4.4 5.0 58.54 5 10 4.0 4.2 29.64 6 7 2.8 3.0 39.71 7 7 2.8 3.0 46.53 8 3 1.2 1.5 23.52 9 9 3.6 3.6 21.65 10 7 2.8 3.0 20.09 11 5 2.0 2.0 32.11 12 4 1.6 2.0 18.44 13 3 1.2 1.5 22.50 14 7 2.8 3.0 19.22 15 10 4.0 4.2 22.86 16 5 2.0 2.0 23.51 17 9 3.6 3.6 35.80 18 7 2.8 3.0 26.50 19 7 2.8 3.0 19.20 20 6 2.4 2.5 26.87 Fig. 4. Distribution of photovoltaic panels for individual self-consumption. for each user is selected considering the limitation of 11 photovoltaic modules per household. Table 6 shows the optimal number of PV panels for each user (139 in total), the installed PV power capacity, the power of the single phase inverter, and the efficiency of the PV installation (𝜂), obtained by dividing the self-consumed energy of each prosumer by the total energy produced by that prosumer, as detailed in Eq. (12): 𝜂=∑ℎ=1 𝐸𝑆𝐶 𝑖,ℎ ∑ℎ=1 𝐸𝑃 𝑉 𝑖,ℎ × 100 (12) In Table 6, differences in plant efficiency for the same number of modules (e.g., Prosumers 3, 6, 7, 10, 14, 18, 19 with 7 modules) arise primarily due to variations in their individual hourly energy consumption profiles relative to the solar generation profile. For example, a prosumer with higher energy consumption during sunshine hours will have a higher self-consumption rate and thus higher plant efficiency compared to a consumer with similar PV generation but higher consumption during off-sunshine hours. The economic data for all users (NPV, IRR, payback) are detailed in Section 3. Fig. 4 shows the distribution of the PV modules in each home. Fig. 5 shows the energy consumed and generated during one year for Prosumers 2, 4, 6, 8, and 10. As can be seen, in some cases during the sunshine hours, the energy generated is greater than the energy consumed, so the surplus energy is injected into the grid. It is also evident that the energy produced by each prosumer is directly proportional to their installed PV power. Prosumer 4 (11 PV modules) generates the highest amount of PV power, while Prosumer 8 (3 PV modules) generates the least. Fig. 5. Distribution of total energy demanded and generated by Prosumers 2, 4, 6, 8, and 10 as a function of hour of day for a whole year. Fig. 6. Total energy demand and generation from the 20 prosumers operating as a whole discretised by hour of the day. 2.7. Design of the photovoltaic system for the energy community This subsection is divided into two cases: (1) users who already have a PV installation and want to form an energy community, Aggregated Community (AC), and (2) users who do not have a PV installation and want to form an energy community, Community from Zero (CZ). In both cases, the 20 prosumers analysed above will constitute the energy community. 2.7.1. Design of the photovoltaic system for the Aggregated community This scenario considers that prosumers already have a PV system in their homes and join together to form an energy community, with a total installation power of 55.60 kWp (139 panels), which corresponds to the sum of the individual installation power of users. The total demand of the prosumers and the total energy generated from all PV systems is obtained from the sum of the individual values of each user and it is represented in Fig. 6. Energy Conversion and Management: X 28 (2025) 101198 7 J.M. Riquelme-Dominguez et al. Table 7 Investment options for the Community from Zero. Number of ... 104 112 120 128 136 144 160 168 ... PV modules 152 Annual PV ... 71546.16 77 049.71 82553.26 88 056.81 93370.78 98 863.18 109 847.97 115340.37 ... generation (kWh) 104355.58 Annual self-consumed ... 30372.42 30 825.67 31220.45 31 571.46 31880.51 32 166.38 32680.65 32 911.12 ... energy (kWh) 32432.72 Annual ... 9678.33 10241.54 10 636.70 10711.75 10 784.28 10837.99 10731.13 10624.14 ... savings (e)10823.75 Investment (e) ... 55189.84 58 986.63 62783.42 68 049.87 71977.17 75 793.45 79 609.72 83 425.99 87 218.33 ... NPV (e) ... 178 414.71 188925.42 196 548.44 198992.80 201 100.18 202859.91 203 451.98 202697.14 201 690.15 ... Fig. 7. Total energy demand and generation from the Community from Zero case, discretised by hour of the day. With the data on energy generated and demanded by the energy community as a whole, the self-consumed energy, the surplus energy, and the amount of energy consumed from the electricity grid are calculated, and the monthly and annual billing values that the community would have to pay in terms of energy are obtained. The economic savings from the benefit of operating the individual PV systems as a whole will be compared in Section 3 to the sum of the individual benefits of each prosumer. If this value is less than the sum of the individual benefits, it means that there is no advantage to working together; otherwise, it is in their interest to combine their installations and operate as an energy community, as they will obtain a higher yield from their installations and, therefore, a higher economic benefit. In the latter case, four sharing coefficients will be analysed for the distribution of the incremental economic benefit to users (difference in savings when considering individual installations or the energy community), depending on: equitable distribution (ED), installed PV power capacity (PC), higher energy consumption during sunshine hours (SS), and the total energy consumption (TC). 2.7.2. Design of the photovoltaic system for the Community from Zero In this scenario, the users are united as a single prosumer, so the photovoltaic power of the whole installation is designed and optimised from zero, considering that the PV installation will have a single three-phase inverter for the whole community. The dimension of the photovoltaic installation considers that the roof surface limits the maximum number of PV modules. Given that there are 20 prosumers and each house can have a maximum of 11 solar panels, a total of 220 photovoltaic modules can be installed. Applying the same criteria used for the size of individual photovoltaic installations, based on the PV energy generated, the self-consumed electricity of all prosumers, the investment, the savings of the installation and its NPV, the optimal number of panels to be installed is 152, as shown in Table 7. Fig. 7 shows the energy generated by the 152-panel PV installation (8 strings of 19 panels connected in series) and a 50 kW three-phase inverter, compared to the total energy consumption of the community. Fig. 8 presents the layout of the PV panels in this case. In an energy community, the primary approach is to treat the community as a single entity with respect to energy purchasing and Fig. 8. Distribution of photovoltaic panels for the Community from Zero case. balance. This means that instead of considering each person or entity within the community as an individual prosumer, the energy trader enters into agreements and contracts with the community as a whole, treating it as a single user. Within the energy community, the leader or administrator must negotiate agreements between all members of the community. In this case, the electricity generated by the photovoltaic installation will be distributed according to the four criteria listed above: equitable distribution, installed PV power capacity, higher energy consumption during sunshine hours, and total energy consumption. Based on this, there may be users who have an excess or deficit of energy within the distribution, so it is considered an internal energy exchange between users, where the price at which energy is sold and bought between community members has to be convenient for all. For this study, the price for the sale and purchase of energy between users has been considered as the price of surplus self-consumption energy for the simplified compensation mechanism (𝑃𝑚 ℎ, i.e., the price at which the retailer compensates the electricity injected into the grid), which is lower than the active energy billing term (𝑃 𝑉 𝑃 𝐶ℎ). The distribution of surplus energy destined for purchase among community members will be carried out among users that have an energy deficit, using the same criteria as previously stated, following an iterative process until the amount of surplus energy destined for exchange among members is over. If the surplus energy in the community meets its allocated limit, but users still have unmet energy needs, they must buy the required energy from the electricity grid. Likewise, any extra energy designated for sale within the community will be shared among users who have surplus energy, using the same distribution guidelines previously outlined. It is important to note that if the difference between the energy from the grid and the energy fed into it is negative, the community does not buy energy from the grid. Therefore, if any prosumer consumes more energy than its share of self-generated energy, it buys from the community at the price of the energy fed into the grid if the community has surplus energy; otherwise, it will have to purchase part from the community and part from the grid. In summary, in the Community from Zero scenario, a prosumer may be in one of the situations described in Fig. 9 depending on the fraction of self-consumption that corresponds to them and whether the energy community buys or sells energy to the grid. As an example, Fig. 9(a) depicts a situation in which the energy community sells the surplus energy to the grid. In this case, Prosumer Energy Conversion and Management: X 28 (2025) 101198 8 J.M. Riquelme-Dominguez et al. Fig. 9. Different situations in which the prosumer can find theirself depending on their own energy balance and that of the energy community: (a) energy surplus; (b) energy deficit. A is able to cover his demand through self-consumption and can sell the remaining energy to the community and the grid simultaneously. Prosumer B, on the other hand, does not cover their energy needs through self-consumption and requires other members of the community (A and C) to supply them with energy. There is a particular case not depicted in Fig. 9(a), where all prosumers have an energy surplus. In that case, there would be no energy exchange between users and all excess energy would be injected into the power grid. Similar conclusions can be drawn from Fig. 9(b) when the energy community has an energy deficit. The computation of each prosumer’s electricity bill is similar to that in the case of individual self-consumption. In this case, the variable term of the bill takes into account the energy purchased from the grid in the hour h (𝐸𝑃 ,𝐺𝑅𝐼𝐷 𝑖,ℎ), the energy sold to the grid in that hour (𝐸𝑆,𝐺𝑅𝐼𝐷 𝑖,ℎ), and the hourly energy sold or purchased from the community (𝐸𝑆,𝐶𝑂𝑀 𝑖,ℎ and 𝐸𝑃 ,𝐶𝑂𝑀 𝑖,ℎ, respectively). The calculation of the variable term of the bill is reflected in Eq. (13): 𝐶𝑉 𝑖,𝑚 =∑ ℎ=1 𝐸𝑃 ,𝐺𝑅𝐼𝐷 𝑖,ℎ ⋅𝑃 𝑉 𝑃 𝐶ℎ−∑ ℎ=1 𝐸𝑆,𝐺𝑅𝐼𝐷 𝑖,ℎ ⋅(𝑃𝑚 ℎ −𝐶𝑏 ℎ) +∑ ℎ=1 (𝐸𝑃 ,𝐶𝑂𝑀 𝑖,ℎ −𝐸𝑆,𝐶𝑂𝑀 𝑖,ℎ)⋅𝑃𝑚 ℎ ≥0 ∀𝑖, 𝑚 (13) Both the fixed term and the total energy bill are calculated in the same way as in the case of individual self-consumption. For the reader’s convenience, Eqs. (14)–(15) are included again below. 𝐶𝐹 𝑖,𝑚 =𝑃𝐶 𝑖,𝑚 ⋅𝑃𝑇 𝑚 (14) 𝐶𝑇 𝑖,𝑚 =𝐶𝐹 𝑖,𝑚 +𝐶𝑉 𝑖,𝑚 (15) The distribution of operating and maintenance costs for the calculation of cash flows is done by using sharing coefficients, which are introduced in the following subsection. This method is also used to determine the PV energy that corresponds to each member of the community, both for self-consumption and for surplus compensation. 2.7.3. Sharing coefficients In an energy community, distribution coefficients are the values or percentages that determine how the energy generated by an energy community is distributed among its users. The importance of the distribution coefficients lies in the fact that they allow for an equitable and fair management of the energy generated. These coefficients can be established according to different criteria, such as the energy consumption of each member, the economic contribution of each member to the infrastructure, or the specific needs of each user. Thus, the share of photovoltaic energy generated by the community corresponding to the prosumer i in hour h is determined as follows: 𝐸𝑃 𝑉 𝑖,ℎ =𝛽𝑖⋅𝐸𝑃 𝑉 ,𝐶𝑂𝑀 ℎ (16) where 𝛽𝑖 refers to the sharing coefficient of prosumer i, and 𝐸𝑃 𝑉 ,𝐶𝑂𝑀 ℎ is the PV energy generated by the energy community in hour h. Similar expressions to those detailed in Eq. (16) are applied to distribute the surplus energy among community members, as well as the operating and maintenance costs, and the initial investment in the installation. In this paper, four methods of sharing among members of the energy community are considered, namely: equitable distribution (ED), installed PV power capacity (PC), higher energy consumption during sunshine hours (SS), and the total energy consumption (TC). •Equal distribution (ED): This method assumes that both the costs and benefits of the community are evenly distributed among the community members according to Eq. (17): 𝛽𝑖=1 𝑛𝑝 (17) •Installed PV power capacity (PC): In this distribution method, the coefficients are proportional to the PV power capacity that each prosumer installs individually (in the case of an aggregated community) or proportional to the power that they would hypothetically have installed separately (in the case of a community from zero). In this case, the sharing coefficients are computed as: 𝛽𝑖=𝑃 𝐶𝑖 ∑𝑛𝑝 𝑗=1 𝑃 𝐶𝑗 (18) •Higher energy consumption during sunshine hours (SS): This allocation strategy aims to account for the use of each prosumer of the collective photovoltaic system by considering their consumption during daylight hours, according to Eq. (19): 𝛽𝑖=∑𝑆𝑆ℎ𝑜𝑢𝑟𝑠 ℎ=1 𝐸𝑃 ,𝐺𝑅𝐼𝐷 𝑖,ℎ +𝐸𝑆𝐶 𝑖,ℎ +𝐸𝑃 ,𝐶𝑂𝑀 𝑖,ℎ ∑𝑛𝑐 𝑗=1 ∑𝑆𝑆ℎ𝑜𝑢𝑟𝑠 ℎ=1 𝐸𝑃 ,𝐺𝑅𝐼𝐷 𝑗,ℎ +𝐸𝑆𝐶 𝑗,ℎ +𝐸𝑃 ,𝐶𝑂𝑀 𝑗,ℎ (19) •Higher total energy consumption (TC): In this case, the sharing coefficients are calculated by dividing the sum of the user’s energy demand over one year by the total sum of the demands of all users. 𝛽𝑖=∑ℎ=1 𝐸𝐿 𝑖,ℎ ∑𝑛𝑝 𝑗=1 ∑ℎ=1 𝐸𝐿 𝑗,ℎ (20) 3. Results In this section, the economic assessment of individual self-consumption and the two modalities of the energy community is presented. The evaluation of the options considered is accompanied by economic indicators that determine the cost effectiveness and feasibility of each investment option. 3.1. Case 1. Individual self-consumption The first case compares the baseline situation, where individuals can only cover their demand with energy from the grid, with the situation where they have a PV system for individual self-consumption. Table 8 presents the energy prices of prosumers without and with photovoltaic systems, together with the individual annual savings due to self-consumed energy. The data in this table are obtained by performing Energy Conversion and Management: X 28 (2025) 101198 9