1 Introduction

The Clean Energy Package (CEP) and the subsequent new Renewables Directive (RED) 2018/2001 [2] empowers the consumers in the system, introducing the concept of renewable self-consumption and energy communities. The CEP introduced new rules on support schemes, to be granted in an open, transparent, competitive, non-discriminatory and cost-effective manner. The added incentive has led to an increased interest in the self-consumption of the renewable energy generated, also in improving the self-sufficiency rates of ECs and individual participants. Furthermore, with the current European energy crisis and inflation, which has resulted in exuberant energy prices, ECs have gained more relevance, especially focusing on self-consumption of locally generated electricity to reduce energy related costs.

1.1 Energy communities in the European context

The development of ECs within the EU member states can be described as at the initial phase, as several key issues still remain unanswered [3]. A study [4] clearly describes the barriers of the transposition of the EU guidelines for ECs into national law, recognizing the need for experts and research to be closely involved with the law making process for EC development. Studies [4, 5] also highly recommend that the full implementation of the CEP rules into national law to be critical for the viability and fast-track development of ECs within the member states. Identification of critical areas to foster the implementation of such regulations would hence be of high significance. An ‘Energy System’ approach towards ECs is also recommended [5], where new business models are introduced in a way ensuring cost-efficiency for all members and real cost savings within the system.

The investigations conducted within the scope of the project R2EC make way for an in-depth analysis of the technical and economic viability of ECs in a European context, identifying key issues towards the development of ECs in the observed regions. In this context, a simulation model and a control strategy based on an extensive implementation software was developed in python. The simulation results were then used for an economic analysis of the observed test beds, investigating the economic viability of the implementation of the R2EC system. Several recommendations for ensuring the viability of the ECs were then derived from these results.

1.2 Energy cell

An ‘Energy Cell’ in the project R2EC is defined as a scalable participant locally generating renewable energy, while providing certain features like flexibilities with high electricity use, to the energy system. An energy cell roughly represents a renewable EC in the European context. The smallest unit in an energy cell is defined to be the household. The R2EC system is designed to allow the interaction of participation on different levels within the energy cell, acting on interfaces on the household, community, cell etc. level.

2 Scenarios and simulation model

2.1 Scenario definition

A multitude of futuristic scenarios for the observed testbeds, including the available flexibilities like e‑heating, HPs, Batteries (ESs), and EVs were designed to be implemented with the simulation model, partly based on the feedbacks from the stakeholder workshops and user surveys, organized at the testbeds [6]. Table 1 illustrates the several scenarios investigated through the technical simulations.

Table 1 General overview of defined scenarios for the designated test beds
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The AT energy cells vary in size, member types, and organisational framework. While two selected Local ECs (LECs) focus on private households, the other two Regional ECs (RECs) are designed to include small businesses and community amenities. Consumption data from 97 private households, 10 small enterprises and 13 community amenities, as well as production data from 26 PV installations were measured, collected, and prepared in 15-minute time resolution, building the basis for the 18 close-to-reality scenarios. The testbeds were simulated for an entire reference year for 3 different frameworks—2021 (actual state), 2030 (near future) and 2040 (far future), with 2021 representing an EC based on actual consumption and production, and 2030 or 2040 assuming increasing Electrification Rates (ERs) and PV penetration in the future.

For BE, two prototypical cells were designed based on measurement data and stakeholder survey results. As the grid injection of surplus generation is incentivized in BE, a public sport center with 200 kWp PV installation is the only participant injecting electricity to the energy community, while the others sell their surplus to the grid. Pools of 100 households with varying consumption levels (A—low, B—medium, C—high, and D—very high), production levels (0—no generation, 1—small, 2—medium, 3—large), some along with flexibilities like EVs and HPs were designed. These pools were simulated with a base-case (no energy cell, no control), with perfect forecasts, data driven forecasts, two different distribution keys, and with consideration of ES. The nomenclature of the household profiles are as described in Table 2.

Table 2 Nomenclature of profiles in the Belgian scenarios
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The NO testbed consists of 20 low energy buildings, each equipped with PV installations. Wind generation (WT) is also considered in half of the scenarios and is shared between the members. With varying ERs and production rates, 4 pools were designed. In total, 16 scenarios were evaluated (4 pools with/without WT, with either individual/collective heating system).

2.2 Simulation model

The simulation model [7] is based on a Multi-Agent System (MAS), where measured consumption and production data were combined with modelled ESs, EVs, HPs, heat storages and buildings. These flexibilities were virtually controlled with the aim of maximal self-consumption [8]. While the operation of flexibilities changes within the simulation, the distribution of electricity follows a static, demand-based distribution key in the designated energy cells. In some BE scenarios, a hybrid distribution key was also tested.

The simulated control is based on the principle of Model Predictive Control (MPC) [9], where a forecast-based optimization is used as the initial point of the control, and deviations between the optimization and reality are accounted for by repeated actualizations. Machine Learning algorithms were used to provide data-driven forecasts. An overview of the control strategy is provided in Fig. 1. The optimization itself is formulated as a Mixed Integer Linear Programming (MILP) problem, with the definition of an objective function [10, 11], as described by Eq. 1, with 24 constraints defining the energy cell system.

Fig. 1
figure 1

Simulation model and control strategy implemented in the R2EC system

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Equation 1 Objective function used in the simulation model

$$min\left({\sum }_{h=0}^{N_{h}}{\sum }_{t=0}^{112}g\_ e\left(h{,}t\right)\cdot ff\left(h\right)+{\sum }_{t=0}^{112}g\_ c\left(t\right)+{\sum }_{t=0}^{112}\left(h{,}bs{,}t\right){,}\right)$$

where

Nh::

Number of houses (members) of the energy cell

h::

running index over houses (energy cell members)

t::

running index over time

g_e::

electricity provided from the grid for each house at each time step (Wh)

g_c::

net amount of electricity provided from the grid taking the entire energy cell into concideration in (Wh)

ff::

fairness factor

ε::

placeholder summarizing various tolerance measures

The solver aims at minimizing the objective function, i.e. minimizing grid purchase of the energy cell and all energy cell members, and thus maximizing individual and collective self-consumption. The MILP problem was formulated and solved in python, where pyomo was installed to create an API to the open source cbc solver [12]

3 Simulation results and evaluation

3.1 Simulation results

The simulation results for all the designated test beds showed increased Self-Consumption Rates (SCRs), both in individual users and within the overall energy cell. The Energy Autarky rates (EARs) are also seen to increase.

In the AT scenarios, a significantly large increase of SCR (~ 30%) from the base case in individual users, and ~ 3% in the overall cell was observed. These significant increases in SCRs were observed particularly in the scenarios involving EVs, which can be charged during the day.

The Fig. 2 visualizes the simulation results for the AT test beds. A decrease in SCRs is observed for the year 2030, mainly due to the massive expansion of PV in the concerned testbeds, leading to higher generation peaks, sometimes even higher than the demand of the entire energy cell. The integration of ESs counterbalanced this effect in the 2040 scenarios, resulting in increased SCRs despite of further increased production in comparison to 2030.

Fig. 2
figure 2

Simulation results showing increased SCRs for the Austrian test beds

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The BE scenarios showed a similar increase in SCRs, with an increase of ~ 32% in individual users, especially in the scenario involving ES. The Fig. 3 illustrates the results for a selected BE pool.

Fig. 3
figure 3

Simulation results for the Belgian test bed, Pool 8

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The SCRs for ‘perfect forecasts’ increase in comparison to the ‘no energy cell’ base case. When considering data-driven forecasts, deviations from reality are accounted for in the operation of flexibilities, which may have resulted in a decrease of SCRs of individual members, and still needs to be solved in the real implementation. When including an additional ES to the sport center, these deviations are compensated, leading to improved SCRs of individual members, and the sport center itself.

The simulation results for the NO testbed also showed higher SCRs, mainly with scenarios involving higher ERs (electric heating, EVs), collective community HPs (C_HP), and WT, as illustrated in Fig. 4. The highest SCR increase of 20% was observed in Pool 1, with a C_ HP and WT. Furthermore, the increased ERs due to EVs led to the increase of SCRs, mainly due to the increased flexibility they provide through charging. It was observed that with C_HP and WT, the SCRs in all pools are significantly high in comparison with the other cases.

Fig. 4
figure 4

Overview of simulation results for the Norwegian test bed

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However, the energy interactions within the energy cell were comparatively low in the NO case, as the consumption profiles and behaviours, along with the generation profiles for WT and PV were quite similar within the energy cell.

3.2 Economic evaluation

The economic analysis was conducted based on Net Present Values (NPVs) and the evaluation is done through the calculated cash flows (i.e., all additional revenue and cost streams in the case of the EC foundation compared to the case without EC), using the simulated results for 20 years. The results of specific scenarios (i.e. different penetration of RE and ERs among the EC members) for each region were selected as the basis for the economic evaluation.

A base assumption of electricity tariff, remuneration for grid injection, community management costs and the equipment costs were assumed, as displayed in the Table 3. The community management costs in each case was assumed to be 50 €/month, and the cost of each control device was assumed to be € 1000 with 10 years of lifetime. The base case assumptions were mainly derived from real EC feedbacks, and could be an overestimation in some cases. The Fig. 5 illustrates the economic evaluation for the selected scenarios in the AT test beds. Under the base assumptions for energy cell management costs, 8 cases were economically attractive (i.e., net positive cash flow). The considered cost assumptions and the addition of ES in the year 2040 (assuming expected cost reduction for ES achievable by that year), and under the considered increase in the ERs within the energy cell appears to be profitable in the region. It was also observed that the scenarios with diverse consumption profiles, including large electricity consumers like small industries and businesses gained the most benefit.

Table 3 Base assumptions for the economic analysis
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Fig. 5
figure 5

Net present value cash flows for selected scenarios in the Austrian testbeds

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In the BE testbed, where support mechanisms [13] for ECs do not include the reduced network tariffs, and no economic interest for individual PV systems to participate in ECs exist [14], the economic benefit to the participants is non-existent under the base assumptions. Fig. 6 briefly summarizes the NPV cash flows for selected BE scenarios.

Fig. 6
figure 6

Net Present Value cash flows for the Belgian testbeds

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Nevertheless, there is a relatively high uncertainty level concerning management costs and equipment costs for energy communities. Assuming lower costs compared to the base case (i.e., € 250 per control device and almost non-existent monthly management costs), economic profitability is reached in 5 out of the 8 investigated cases. The main reason for the original base case to be not profitable is the restrictive regulatory framework in BE. This prevents a sufficient economic value creation for a viable Business Model. Furthermore, the absence of large electricity consumers like commercial consumers combined with the presence of prosumer who already self-consumed from their own PV systems limited the overall self-consumption potential within the energy cell, which could also be the reason for the smaller economic gains.

With no EC-specific regulatory framework in place, and very similar consumption and generation profiles within the EC participants, the NO testbed was understandably the least profitable in comparison. The NPV cash flows under the original base assumptions for the NO scenarios are as shown in Fig. 7. Considering that the main objective of the test bed establishment was to check the self-sufficiency of such an energy cell/EC in the country, and to promote participation, the improved individual SCRs with the designed system is already highly economically attractive. This can hardly be improved by creating an energy cell/EC, without the proper regulatory framework.

Fig. 7
figure 7

An overview of the economic evaluation of the observed Norwegian testbeds

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Again, given the high uncertainty level concerning management costs and equipment costs for ECs in NO, a sensitivity analysis was conducted, which showed that half of the considered cases were economically viable, with a reduced community management costs of 25 €/month and € 500 per control device. With a further reduced cost of € 250 per control device, all of the considered cases were observed to reach break-even, and be economically viable.

4 Discussion

From the technical simulation results, and the economic analysis it was evident that there were advantages of founding an energy cell/EC with the R2EC system in the observed test beds. The Table 4 illustrates the quantification of these positive effects in the observed testbeds. These advantages are seen to be strongly dependent on the generation and consumption patterns of the individual energy cell members. For the AT scenarios, the SCRs of the overall energy cells increased ~ 20–~ 60%. The EARs thereby are also observed to be improved by ~ 6–~ 12%. The BE testbeds showed similar results despite having different framework conditions than AT. The foundation of an EC is observed to increase the SCRs by 19–22% in the investigated scenarios, and EARs improving by 10–13%. In the NO case, as each individual in the energy cell was designed to have a PV installation (in AT & BE, there are always pure consumers in the mix), the quantified advantages were observed to be comparatively lower. The SCRs are seen to increase by 10–13% and the EARs by 8–11%.

Table 4 Quantification of positive effects of establishing an EC
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The economic evaluation of the testbeds also highlighted some of the underlying advantages in the implementation of the R2EC system in the observed regions. The AT testbeds were the most economically attractive, with 8 out of 16 investigated cases having the net positive cash flows with the initial base assumptions. In the BE and the NO testbed regions, modifications to the base case were needed to achieve sufficient economic gains. With the current framework, it is clearly understandable that the EC management and other manageable costs could and should be optimized and responsibilities delegated within the cell/EC. Though studies [15] recommend outsourcing EC management roles to third parties for efficiency, with the appropriate knowledge dissemination and training, EC management roles could be fulfilled by EC members and could result in monetary savings within the EC. With a strategically planned regulatory framework development in place, and the appropriate cost allocations, it is evident that there can be economic benefits both to the individual participant, and also the overall EC.

5 Conclusions

The simulation results and analysis, along with the economic evaluations were presented to the stakeholders in the respective testbeds through the co-creation workshops. On one hand, it was recognized that there were both technical and economic advantages in implementing the R2EC system in the observed testbeds. Despite the insufficient economic advantages (BE and NO), the continued interest of the stakeholders in their participation in an EC, shows that with the right regulatory framework and support mechanisms, the development of ECs within the investigated regions would accelerate considerably and their overall energy sector would benefit both technically and economically. On the other hand, these technical and economic evaluations could not only be a reference for the design of a self-sufficient and economically beneficial energy cell/community, but also be a basis for EC regulatory frameworks evolution in the respective countries.