This research raises the possibility for households in energy poverty to participate in shared photovoltaic systems in renewable energy communities (REC) to reduce their energy costs, with investment costs covered by public institutions. It begins by evaluating the current solution for vulnerable households, which relies on public subsidies to lower energy costs without addressing root causes or improving environmental impacts. The study compares traditional subsidies with REC participation for vulnerable households. By simulating a REC composed of such households, the results indicate that REC participation is more cost-effective for public institutions than energy subsidies. At the economically optimal size of 31 kWp, the cost of subsidies decreases by 58,000 €, a 50% reduction, with household savings increasing by 6%. At 58 kWp, the need for additional support checks is eliminated, increasing household savings by 65% but with a lower NPV of 22,500 €. The largest viable system, 75 kWp, increases average household savings by 82%. This approach also leads to a net reduction in GHG emissions, engaging previously excluded households in the energy transition.

This study explores the complexities surrounding the adoption of decentralised Renewable Energy Technologies in Spain, crucial for transitioning towards a renewable energy-driven economy. Through a systematic review of both scientific and grey literature, key factors influencing adoption were identified. Utilising the Analytic Network Process method, this research highlights political will, technological maturity, and fiscal incentives as primary drivers. These findings underscore the importance of mature technologies and incentivisation strategies in expediting adoption. Conversely, addressing barriers necessitates a multifaceted approach, presenting challenges for policy formulation. However, the analysis reveals a positive cascade effect, wherein strengthening primary drivers positively impacts others within their domain. This pattern is mirrored in the barriers. Furthermore, the study reveals consistent factors across technologies, adopter types, and regions. The three-axis analysis shows the largest differences in terms of the type of adopter, followed by the type of technology. The smallest differences are found by region, emphasising the unifying influence of the EU framework. This suggests a unified approach to policy design and promotion efforts.

This study develops a methodology to characterise and forecast large consumers’ electricity demand, particularly municipalities, with hundreds of different metered supply points based on the previous characterisation of facilities’ consumption. Demand forecasting allows consumers to improve their participation in electricity markets and manage their electricity consumption. The method considers a classification by different types of metered supply points combined with artificial neural networks to obtain hourly forecasts using well-known parameters such as day types, hourly temperature, the last hour of electricity consumption, and sunrise and sunset time. We apply the methodology to the municipality of Valencia using over five hundred hourly load profiles for a year during 2017 and 2018. Our results present aggregated forecasts with a maximum Mean Absolute Percentage Error of 3.8% per day, outperforming the same forecast without classifying Metered Supply Points. We conclude that a correct electricity demand forecast for a consumer with different types of consumption does not need submetering, but characterising Metered Supply Points is an option with lower costs that allows for better predictions.

The authors regret <To not have included during the publication process the acknowledgement to a project that has been part of this work and want to include in the acknowledgements part: This work was partially supported by the Grant TED2021-129722B-C31 (ALIVE-DER), funded by MCIN/AEI/10.13039/501100011033 and by “European Union NextGenerationEU/PRTR”. >. The authors would like to apologise for any inconvenience caused.

Identifying districts’ potential to become Positive Energy Districts (PED) is challenging but strategic since they are considered critical enablers for cities’ carbon neutrality. PEDs are city areas with a positive annual energy balance, achieved primarily through energy efficiency and renewable energy generation while ensuring sufficient energy flexibility. This investigation introduces a methodological framework designed to prioritise and comprehend the potential PED status of diverse districts within a city, drawing upon predetermined criteria and expert insights. The study employs a combination of Decision Making Trial and Evaluation Laboratory (DEMATEL) and Analytic Network Process (ANP) methodologies to scrutinise the city's various districts and the influencing criteria. The method's applicability is tested through to the specific case of Valencia City. The study reveals that the importance of specific criteria in attaining PED varies according to the distinctive attributes of each district. Furthermore, variations emerge based on the perspective and expertise of the contributing experts. The results of this application allowed the selection of the 19 most influential criteria, organised into technical, social, urban, environmental and economic clusters. The two economic criteria (Investement and Grant or projects), one social criterion (Interest or acceptance) and one technical (Potential for retrofitting the buildings), are the most influential overall. The evaluation of the 19 administrative districts of Valencia for each criterion allowed the identification of the districts on the city's outskirts as having the greatest potential to be energy-positive. In conclusion, the proposed methodology aids decision-making in a city's urban energy planning on a district-by-district basis.