Towards Sustainable Energy Communities: Integrating Time-of-Use Pricing and Techno-Economic Analysis for Optimal Design—A Case Study of Valongo, Portugal

Abstract

This paper presents a comprehensive analysis of optimal energy community design, leveraging time-of-use pricing mechanisms and techno-economic parameters. Focusing on a case study of Valongo, Portugal, this study explores the intricate interplay between energy infrastructure planning, economic considerations, and pricing dynamics. Through a systematic approach, various factors, such as renewable energy integration, demand–response strategies, and investment costs, are evaluated to formulate an efficient and sustainable energy community model. Time-of-use pricing schemes are incorporated to reflect the dynamic nature of energy markets and consumer behavior. By integrating techno-economic analyses, this study aims to optimize energy resource allocation while ensuring cost-effectiveness and environmental sustainability. The influence of optimized sizes of photovoltaics (PV), battery storage, and electrical vehicles (EVs) on self-sufficiency rates, self-consumption rates and CO₂ savings is analyzed. The findings offer valuable insights into the design and implementation of energy communities in urban settings, highlighting the importance of adaptive strategies in the transition towards a resilient and low-carbon energy future. The novelty of this paper lies in its comprehensive approach to energy community design, which integrates time-of-use pricing mechanisms with techno-economic parameters. By focusing on the specific case of Valongo, Portugal, it addresses the unique challenges and opportunities present in urban settings. Additionally, the analysis considers the interaction between renewable energy production, demand profiles and investment costs, providing valuable insights for optimizing resource allocation and achieving both cost-effectiveness and environmental sustainability.

1. Introduction

Community involvement in energy projects is reshaping the energy landscape by motivating active citizen engagement. Community energy initiatives encourage collaborative actions throughout the energy system, sparking interest in diverse project management practices [1]. Energy communities offer various benefits to energy systems, such as supporting system operations locally, reducing the need for traditional network upgrades and potentially lowering energy prices for customers. Moreover, citizen participation can facilitate access to private capital for renewable investments. Reference [2] presents a review and research opportunities at the crossroads of technical design, policy, business models and economics.

According to the Clean Energy Package of the European Union, local and renewable energy communities are promising for the efficient use of distributed energy technologies at regional levels. However, realizing these communities faces limitations, particularly during the planning phase, where numerous decision variables must be considered based on community participants and distributed technologies [3]. Energy communities are still relatively uncommon, but their growth depends on having enough money, knowing how things work technically and being able to start and run businesses [4,5,6,7].

Scaling-up energy community business models requires demonstrating their financial and non-monetary benefits for members and society, along with providing guidance to achieve benefits and avoid failures [8,9]. A methodology to optimize local energy communities in Spain with battery energy storage systems with different capacities is shown in [10]. A methodology with a mixed-integer linear program for assessing the implementation of multi-energy systems in an energy community in multi-family buildings in Germany is presented in [11]. However, none of these papers consider real-time pricing. Reference [12] investigates holistic modelling and simulation approaches for energy communities with grid and market layers, which gave us the idea for this research. In addition, it has to be stated that big data play an important role in optimizing the operation and control of energy communities. Big data are widely recognized as being some of the most powerful drivers to promote productivity and improve efficiency [13]. Several other case studies have been performed to analyze the effectiveness of different energy communities. The authors demonstrated the key parameters influencing the participation of citizens in energy communities and indicated that there is the possibility to further expand the capacity installed without undermining the profitability of investment, as well as a potential CO₂ emission reduction of over 80% in the case of PV and electrical vehicle communities [14,15].

This paper presents an optimal planning approach for renewable energy communities in Valongo, Portugal, aiming to serve as a reference for researchers and professionals in designing and implementing Net-Zero Energy Communities (NZECs) across diverse climatic and location-based conditions. This paper details strategies for mitigating solar power plants, adapting household buildings, employing battery storage technologies and implementing smart management systems. The case study considers real annual energy consumption measurements and climatic conditions for PV plant production estimation to optimize the size of PV plants and battery storage systems, considering energy pricing schemes and future development of EV charging infrastructure within the energy community. The major contributions of this paper are as follows:

• An integrated approach to energy community design by Genetic Algorithms using time-of-use pricing with techno-economic parameters.
• Adaptive strategies for solar integration into distribution power systems, considering EV charging infrastructure, highlighting the importance of flexibility and scalability in designing resilient energy communities.
• This report serves as a valuable reference for communities aiming to achieve net-zero carbon emissions while optimizing energy resource allocation and promoting economic viability.

The novelty of this paper lies in its comprehensive approach to energy community design, which integrates time-of-use pricing mechanisms with techno-economic parameters. By focusing on the specific case of Valongo, Portugal, it addresses the unique challenges and opportunities present in urban settings. Additionally, the analysis considers the interaction between renewable energy production, demand profiles and investment costs, providing valuable insights for optimizing resource allocation and achieving both cost-effectiveness and environmental sustainability.

2. Energy Community Description

The analyzed renewable energy community is planned to be implemented in Valongo, Portugal. The community comprises a total of fourteen municipality-owned buildings (Figure 1), namely the following: (1) a sports center, (2) a pool, (3) a library, (4) a high school, (5) Vallis Longus school, (6) Valado school, (7) Biscoito and Regueifa museum, (8) Biscoito and Regueifa restaurant, (9) a municipal museum, and (10) EV chargers. Among these buildings, Valado school, the municipal pool and the library received PV installations for self-consumption at the end of 2022. In addition, some of the remaining facilities can also be considered for future PV installations and operate as prosumers. Finally, the existing EV charging stations can be used as storage and to exploit demand side flexibility.

Table 1. Generation assets.

Identification	Type of Installation	Installed Capacity (kW)
Valado school	PV plant	40
Municipal library	PV plant	15
Valongo’s pool	PV plant	30

,

Table 2. EV and storage assets.

Identification	Type of Installation	Installed Capacity (kW)
EV charging stations	Charging station/storage	4 × 22 kW

and

Table 3. Consumption assets.

Identification	Profile	Contracted Power (kW)	Annual Energy Consumption (MWh)
1. Valongo sports center	Commercial	41.4	53
2. Valongo’s pool	Commercial	55	292
3. Valongo library	Commercial	80	103
4. Valongo’s high school	Commercial	92	88
5. Vallis Longus school	Commercial	82	801
6. Valado school	Commercial	41.4	81
7. Biscoito and Regueifa museum	Commercial	41.4	20
8. Biscoito and Regueifa restaurant	Commercial	41.4	6
9. Valongo municipal museum	Commercial	41.4	49
10. EV chargers	Commercial	88	10

show the main characteristics of the generation, consumption and storage assets of the energy community. The listed assets represent only the currently available production, consumption and storage assets, without focusing on the additional assets that may be added in order to optimize the operation of the energy community. These additional assets will be analyzed later in this paper when proposing the optimal design of the community.

Figure 1 illustrates the dispersion of all locations across the city, necessitating the utilization of the utility distribution grid for energy sharing. However, this reliance on external infrastructure imposes an additional financial burden, as grid fees must be paid for energy transfer. To address this challenge, it is imperative to meticulously analyze local grid tariffs to prevent both underestimation and overestimation of the grid costs for any injected or consumed kilowatt-hour (kWh).

3. Data Analysis

Consumption data and the estimation of generation data were collected from the Valongo location. The data were used for production and consumption analysis, electric energy balance and the proposal for energy community improvement to optimally achieve the objectives.

It has to be mentioned that hourly values of demand power were collected from each location using metering data for one year, resulting in 8760 demand values for each location. However, PV production was not measured at these locations; it was estimated using the exact site latitude and longitude and the PVGIS online tool [16].

3.1. Electric Energy Consumption

According to the measurement data on the site, average daily diagrams for winter and summer were created for each community participant. Figure 2 shows daily demand curves for these periods. It has to be stated that the diagrams shown in Figure 2 represent average winter and summer diagrams. These average diagrams were calculated using the average demand values for each hour during all winter and all summer days. With this in mind, the diagrams are only illustrative of the diagram shapes during winter and summer, and do not identify critical cases which would probably be the hottest summer and the coldest winter day.

In addition, a total annual consumption diagram is given (Figure 3) to identify maximum and minimum loads. From Figure 3, it was found that the maximum load was 285 kW in August, while the minimum load was 39 kW in January.

3.2. Electric Energy Production

The production data were analyzed according to PVGIS measurement data for the location of Valongo, which has a latitude and longitude of 41.19° North and 8.50° West. According to the currently existing plans, three PV power plants were considered, which have an installed DC power of 15 kW, 30 kW and 40 kW, for use as the Base case Scenario in further analysis.

The first set of production analysis was conducted for horizontal plane PV panels. Other than knowing solar irradiation at the location of PV and installed PV power, it is crucial to include other relevant data that will affect the production of the power plants. For this purpose, we considered module mismatch losses, panel contamination losses and potential increased production due to the surroundings’ reflection of solar irradiation. All of these parameters of the calculation are given in

Table 4. Calculation parameters.

Site Latitude [°]	Site Longitude [°]	Tilt Angle [°]	Azimuth Angle [°]	PV Panel Mismatch Losses [%]	PV Panel Contamination Losses [%]	Surroundings Reflection Coefficient
41.19	−8.50	0	0	3	3	0.2

. The simulation resulted in a set of diagrams showing individual and total production of the PV power plants. The diagrams are shown in Figure 4 and Figure 5. It was concluded that the estimated total annual productions of PV systems are as follows: 22.84 MWh for the 15 kW system, 45.69 MWh for the 30 kW system and 61.08 MWh for the 40 kW system, making the total 130 MWh/year.

Using an optimization Genetic Algorithm, the optimal tilt angle was estimated to be 35° to extract maximum power from the installed PV panels. The results show that each year a 15 kW system can produce 26 MWh, a 30 kW system is estimated to produce 52 MWh, and a 40 kW system produces around 70 MWh. This makes a total of 148 MWh/year, which is around 12% higher than the horizontal PV panels. Figure 6 shows a comparison of the horizontal and optimal scenarios.

3.3. Pricing Model

For all scenarios and buildings, the same 4 h pricing model was used, as indicated in

Table 5. Pricing models.

Pricing Model	Summer Time	Winter Time
Peak (Ponta)	9:00–10:30	10:30–13:00
	18:00–20:30	19:30–21:00
Full (Cheia)	8:00–9:00	8:00–10:30
	10:30–18:00	13:00–19:30
	20:30–22:00	21:00–22:00
Normal Empty (Vazio Normal)	0:00–2:00	0:00–2:00
	6:00–8:00	6:00–8:00
	22:00–0:00	22:00–0:00
Super Empty (Super Vazio)	2:00–6:00	2:00–6:00

[17]. The pricing model in Valongo comprises four distinct prices throughout the day, with varying pricing periods in both summer and winter seasons. While this model is accepted locally, it is important to note that in certain countries, temporal differences are not the only consideration; spatial variations are also integrated into pricing structures. Spatial discrepancies in timing are critical to mitigate the risk of high peak demand nationwide coinciding with transitions between high and low electricity prices.

One effective solution involves staggered timing adjustments across different regions of the country, such as shifting pricing periods by 30 min or 1 h. This approach ensures that peak demand is distributed more evenly across the grid, reducing strain during price transitions and promoting greater grid stability. By implementing spatial differences in timing, countries can optimize energy consumption patterns and enhance overall grid efficiency, ultimately benefiting both consumers and the electricity system as a whole.

Table 5 shows timings during the day when different pricing models are applied. Peak periods have the highest prices, followed by Full periods. Periods of low demand and higher production have lower prices, such as during Normal Empty and Super Empty periods. The actual prices during these periods are given and explained in Figure 7.

The pricing model consists of three components:

• Commodity (abbreviated LM).
• Network (abbreviated ATR).
• Taxes.

Figure 7 illustrates the commodity prices. Each color in Figure 7 represents different months, as per annual commodity and network prices (the top diagram in Figure 7). The same colors are kept to show weekly and daily changes in electricity prices in each month. Different timings of high and low prices are evident in the summer and winter months, as defined in Table 5, with each day having four different price levels. Specifically, electrical energy is less expensive during off-peak hours, which span from 22:00 to 8:00 (identified as Vazio zones in Table 5). The network costs exhibit a similar trend to the commodity costs. To simplify the analysis, a fixed tax rate of 23% was applied to both the commodity and network costs.

Considering the pricing models, the total daily price paid by the customer is calculated as

$$C={\sum }_{t=1}^{24}{P}_{avg}\left(t\right)·\left(CP\left(t\right)+NP\left(t\right)\right)·\left(1+T\right)$$

(1)

In the above equation, C is the total daily cost of electricity supply, P_avg is the average hourly power, CP(t) is the grid commodity price, NP(t) is the grid network price and T is the common tax rate (0.23 in the considered case).

4. Electric Energy Balance, the Base Case

Electric energy balance is one of the most important analyses to show the adequacy of PV installation size, battery size, production–consumption correlation, and energy exchange with the utility grid. Additionally, this analysis is very important for further economic analysis and optimization of the energy community system. Figure 8, Figure 9 and Figure 10 represent electric energy balance results considering the existing 85 kW horizontally placed PV system. Only on rare occasions is PV production higher than the load; therefore, the installation of a battery system is not justified. It is concluded that the PV system reduces the consumed grid energy from 915 MWh/year to 785 MWh/year. Considering the pricing model given in Figure 7, the planned PV system of 85kW can save up to 26.000 EUR/year.

Figure 11 shows individual load and PV diagrams of three locations that have photovoltaic systems. It can be seen that the swimming pool has excess electric energy during summer. On average, this energy is around 140 kWh/day. Valado School has less excess electric energy on an average summer day, around 100 kWh/day. This energy will be injected back to the grid to other community consumers. In summer, the library’s PV system matches the consumption, leaving no excess electric energy. In winter, all of the systems consume their entire PV production.

For the sake of further analysis, the self-consumption rate (SCR) and the self-sufficiency rate (SSR) were defined. The SCR shows the percentage of PV production locally consumed (not injected to the grid), while the SSR shows the percentage of consumption covered by local PV production (not taken from the grid). The SCRs shown in

Table 6. SCR and SSR of individual PV sites.

Building	Grid [MWh/Year]	Cons. [MWh/Year]	Injection [MWh/Year]	SCR [%]	SSR [%]
Valado School	22.8	38.3	15.5	74.92	54.11
Municipal Library	76.0	78.2	2.1	89.85	19.57
Valongo’s Pool	188.8	188.9	0.1	99.84	19.11

indicate that most of the produced electric energy can be consumed locally. The SCR indicates that there is still room for additional solar production, especially when electric energy sharing is enabled among energy community members. The self-consumption rate and the self-sufficiency rates are calculated on annual level, as follows:

$$SSR=\frac{\left({E_{cons}}_{total}-{E_{cons}}_{grid}\right)}{{E_{cons}}_{total}}·100\%$$

(2)

$$SCR=\frac{\left({{E_{PV}}_{total}-E}_{inj}\right)}{{E_{PV}}_{total}}·100\%$$

(3)

In the above equation, E_cons,total is the total annual consumption, E_cons,grid is the total annual electric energy consumed from the grid, E_PV,total is the total annual PV production and E_inj is the total annual electric energy injected to the grid.

Table 7. Results for Local Energy Community (LEC) with 3 buildings with PV.

Building	Total Cost [EUR]	Consumption Cost [EUR]	Production Revenue [EUR]
Valado School	7933	11,554	3621
Municipal Library	24,795	25,358	563
Valongo’s Pool	55,560	55,566	6
Total	88,288	92,478	4190
3-building LEC	84,956	85,810	854
Potential savings	3332	6668	−3336

shows the potential of electric energy sharing between the three buildings that have solar installations, where the injection of one building is intended to be matched with the consumption of the other buildings. The potential savings are EUR 3332, which represents about 4% of the total bill. Given that the total self-consumption rate goes up to 98.5%, including more buildings would not significantly improve the results, since almost all solar PV production would already be self-consumed by this three-building Local Energy Community (LEC).

For the rest of the analysis, an LEC including all buildings and all installed solar PV systems is considered (Base case LEC). The results are summarized in

Table 8. Results for LEC of all buildings.

Scenario	Total Cost [EUR]	Grid Balance [MWh]	Demand [MWh]	Inject [MWh]	PV [MWh]	SCR (%)	SSR (%)	CO2 Savings (tCO2)
Base case LEC	303,683	874	999	0.0	125	100.0	12.5	29.6

. In this case, all solar PV production would be consumed by the LEC (SCR of 100%). Based on the self-sufficiency rate (SSR) of 12.5%, the potential for additional solar PV systems to increase the SSR is significant. This is discussed in the next section. Table 8 also shows the total CO₂ savings based on the estimated CO₂ emission of the Portuguese power system, which has an annual average of 154 gCO₂eq/kWh [18]. These savings are calculated as follows:

$${{CO}_2}_{savings}={{CO}_2}_{intensity}·E_{con{s}_{total}}·SSR/100$$

(4)

In Equation (4), CO_2savings represents the total annual saving of CO₂ emissions (due to electrical electric energy not taken from the grid); CO_2intensity represents the average annual carbon intensity of the grid (154 gCO₂eq/kWh for Portugal in 2023).

5. Energy Community Improvement Proposal

In order to achieve electric energy independence, more generation assets have to be installed. For this purpose, a scaling analysis was performed to show the dependence between installed PV power and utility electric energy exchange (Figure 12). It can be seen that, in the case of an optimal tilt angle (35°), the required PV installation is 530 kW to achieve electric energy independence, provided that battery storage is used. From Figure 12 (right), it can be calculated that 470 MWh/year of electric energy has to be stored and redistributed. For this purpose, to eliminate utility grid exchange, it is required to have substantial electric energy storage capacity. Figure 13 shows the required capacity using SoC diagrams in the case of unlimited charging power and in the case of seven different battery chargers. It can be easily concluded that the total storage capacity should be at least 110 MWh and that it should be charged using battery chargers with a total charging power of 450 kW. This solution would not be economically feasible due to high battery prices.

Table 9. LEC with more PV and battery storage.

PV [kW]	With Battery System
	Estimated CAPEX [EUR]	Total Grid Energy [%]	Storage Capacity [MWh]	Charging Power [kW]	SCR [%]	SSR [%]	CO2 Savings [tCO2/Year]
100	145,000	81	0.15	50	100	19	41
200	416,000	62	0.72	150	100	38	81
300	1,260,000	43	3.2	250	100	57	122
400	5,020,000	24	15.4	350	100	76	163
500	21,500,000	5	70.0	440	100	95	204
530	30,530,000	0	100	450	100	100	214

and

Table 10. LEC with more PV without battery storage.

PV [kW]	Without Battery System
	Total Cost [EUR]	Cost Savings [EUR]	Grid Balance [MWh]	Inject. [MWh]	PV [MWh]	SCR [%]	SSR [%]	CO2 Savings [tCO2/Year]
Base case LEC	303,683	-	874	0.0	125	100.0	12.5	29.6
BC+ 100 kW	237,446	66,237	727	9.6	272	96.5	26.3	62.2
BC+ 200 kW	180,942	180,639	580	59.7	420	85.8	36.0	85.4
BC+ 500 kW	48,303	255,380	138	369.0	861	57.2	49.3	116.7
BC+ 1000 kW	−139,709	443,392	−598	1051.5	1597	34.2	54.6	129.4

show dependence between the percentage of electric energy taken from the utility grid and PV system power, the storage capacity and the storage charging power, as well as CAPEX for each case considering 1000 EUR/kWp of PV and 300 EUR/kWh of storage. It is obvious that cases with 400 kW and more require really high investments that are not financially feasible. With future drops in battery prices, solutions with lower required storage capacity should be considered. By multiplying the electric energy not taken from the grid with the CO₂ intensity of the grid, the total CO_2,eq savings are calculated. Table 9 analyzes cases with battery storage systems, while Table 10 shows the results of additional PV installations without battery storage systems. It can be seen from Table 10 that a cost savings of EUR 66,000 to 444,000 can be achieved. Taking into account a total CAPEX of 1000 EUR/kWp for the solar PV investments, this leads to total investment of EUR 1 million and a simple payback of approx. 1.5 to 2.5 years.

As the final step, a Genetic Algorithm was used to find the optimal PV+battery storage system size taking the PV installation price, battery prices and utility electric energy prices into consideration. The price range for electrical energy storage systems varies depending on several factors, including the type of storage technology, its capacity and the specific application requirements. Generally, battery storage systems, such as lithium-ion batteries, dominate the market and typically range from hundreds to thousands of EUR per kilowatt-hour (kWh) of storage capacity. However, prices have been steadily declining in recent years due to technological advancements and increased production scale. It is important to consider not only the initial investment cost but also factors like lifecycle costs, efficiency and system reliability when evaluating energy storage options. As the market continues to evolve and technology improves, it is expected that the price range for electrical energy storage will continue to decrease, making storage more accessible and widespread across various sectors of the energy industry. This analysis only illustrates that the optimal case, regarding the total system cost for a 20-year system lifetime, may not be the case with zero-electric-energy exchange, considering high electric energy storage prices. The optimization results are given in Figure 14 for the economic parameters from

Table 11. Economic parameters for optimization.

PV Installation Price [EUR/kWp]	Energy Storage Price [EUR/kWh]			Utility Energy Price [EUR/kWh]	System Life [Years]
1000	Case1 (low)	Case2 (medium)	Case3 (high)	Defined in Figure 7	20
	100	300	600

. It can be seen that for three different cases of battery prices, the optimal PV sizes are 230, 250 and 310 kWp.

6. Discussion

The optimization of energy communities integrating photovoltaic (PV) generation, storage systems and time-of-use (TOU) pricing represents an important step towards sustainable and resilient energy grids. Beyond the conventional focus on minimizing grid dependence, it is important to also investigate the broader economic benefits of energy communities considering the interaction of the communities with the grid. By actively participating in the provision of ancillary services and demand response mechanisms, energy communities not only enhance their self-sufficiency but also emerge as dynamic stakeholders, helping to optimally operate the distribution grid and increase its hosting capacity.

Through intelligent coordination and the utilization of distributed energy resources, such communities have the potential to alleviate the strain on the grid during peak demand periods, enhance grid stability and potentially even generate revenue streams. Moreover, the integration of local energy sharing policies introduces a sophisticated layer of complexity. Energy sharing policies in energy communities typically involve regulations and agreements that facilitate the sharing of surplus energy among members.

These policies often outline the terms for peer-to-peer energy transactions, ensuring fair compensation for energy producers and promoting renewable energy utilization. They may include mechanisms for metering and billing, fostering a cooperative and sustainable approach to energy distribution within the community. While fostering community resilience and promoting equitable energy access, such policies also entail detailed cost considerations.

This paper only considers self-consumption and self-sufficiency rates, as requested by the stakeholders. However, further development of the study should definitely move in the direction of analyzing the broader economic benefits of the grid and the impact of local energy sharing policies and costs. This broader analysis could encompass aspects such as the potential for revenue generation through participation in energy markets, the impact on local economies through job creation and investment and the societal benefits of increased resilience and energy equity. Additionally, exploring the potential synergies between energy communities and other stakeholders in the energy ecosystem, such as utilities and regulatory bodies, could provide further insights into the optimization of sustainable energy grids.

7. Conclusions

This pre-feasibility study provides a comprehensive technical analysis of the potential energy community, drawing upon measurement data of electrical energy consumption, a basic description of the community and electricity bills. The key findings highlight the effectiveness of the planned 85 kW PV system in reducing annual electric energy consumption by 15%. Furthermore, optimizing the tilt angle to 35° enhances production by 12%.

However, challenges arise in winter when PV production falls short of covering the consumption of certain facilities equipped with rooftop systems, such as the swimming pool, library and school. Conversely, in summer, surplus PV production from these facilities can adequately supply other energy community participants.

A deeper analysis was conducted to explore achieving a zero-electric-energy system. It was determined that the complete electric energy independence of the community could be attained with a 530 kW PV system. However, to avoid exchanging electricity with the utility grid, substantial storage totaling 110 MWh with 450 kW of charging power would be necessary. Presently, this solution is not financially viable, indicating the need for further exploration and potentially innovative solutions to address the economic feasibility of achieving energy independence. Additionally, ongoing advancements in energy storage technologies and cost reductions may render such solutions more financially viable in the future. Continual monitoring of market trends and regulatory developments will be essential to seize opportunities for cost optimization and to ensure the long-term sustainability of the energy community’s objectives. Moreover, while the feasibility of complete energy independence remains a compelling goal, the financial constraints underscore the importance of considering alternative strategies. Exploring options such as demand-side management, grid interaction agreements and innovative financing models could provide avenues to mitigate the high upfront costs associated with large-scale storage solutions.

Further analyses were conducted to come to the following conclusions:

• If PV and battery storage are selected to maximize electric energy independence, a self-sufficiency rate of more than 50% and a self-consumption rate of 100% can be achieved optimally with 250 kWp of PV and corresponding battery storage capacity.
• Additional solar PV installations are both financially interesting and important to increase the LEC’s electric energy sharing potential, self-sufficiency rate (SSR) and CO₂ savings, e.g., investing in an additional 1000 kWp of solar PV (without battery storage) for the LEC could result in an SSR of approx. 55%. Of course, the required space to do so must be available and grid connection capacities need to support such an expansion of PV production.
• The increase in electrified mobility will have an important impact on the electricity demand of the LEC, increasing even further the importance of local electricity production. Additionally, intelligent steering of these flexible assets is crucial to reduce costs and make optimal use of local solar production (and thus increase the LEC’s SCR, SSR and CO₂ savings).
• Thanks to the current pricing opportunities, battery electric energy storage (BES) is interesting for increased self-consumption and arbitrage. The optimal BES size depends heavily on the ambitions with respect to the other components such as PV and EV. Note that potential additional value streams that could be enabled through BES, such as balancing market or imbalance settlement, have not been taken into account.
• Local legislation regarding electric energy sharing (and the costs related to its implementation) needs to be further taken into account to reach more detailed and accurate results.
• It is necessary to conduct detailed economic analysis and combine it with technical study to perform optimization procedures and obtain the best system size. Some of the main aspects of the community to be considered are the condition of electric energy exchange with the grid (prices and constraints), ancillary services of the community, electric energy storage options, etc.
• The benefit of the LEC can be seen in the correlation between consumption and production diagrams. While the correlation of individual participants ranges between 0.5 and 0.85, the correlation of the entire community is 0.87. This shows that, relatively speaking, the LEC has higher self-sufficiency and lower required battery storage than individual participants.