Technology readiness levels (TRLs) is a well-established and widely used approach for defining the readiness of new technology. It assesses technology maturity against specific benchmarks, ranging from level 1 (concept) to level 9 (market solution). Although this is a useful classification service that allows us to establish a common language, there are cases where we find that this conceptual approach cannot adequately highlight the maturity of certain innovative endeavors and effectively steer their development to higher TRLs. We will present an empirical case where the TRL approach presented a critical shortcoming in highlighting the true and effective readiness of a specific technological development and could not suggest the next natural step in ascending the maturity ladder. We will seek to generalize for the case of co-creation at large, analyze why co-creation may be poorly serviced by the current TRL model, and suggest an amendment that would allow the observed shortcomings of the traditional TRL approach to be overcome and its use extended into such co-creative settings, thus allowing stakeholders to enhance the effectiveness and impact of their collaborative innovation efforts.

This paper presents a practical methodology to facilitate decision-making for the development of a renewable energy community (REC) in the municipality of Hersonissos in Crete, Greece. The impact of energy sharing produced from renewables through direct or indirect citizen participation presents an enormous opportunity to strengthen energy democracy and to alleviate energy poverty. Triggered by the latter being amongst the most essential goals for implementing policies towards low-carbon economies at the local level, the current study presents a step-by-step methodology to facilitate decision-making for building a municipality-led REC. Initially, potential sites and alternative sittings of photovoltaic (PV) plants are explored for detecting the optimal installation with respect to technical and possible legislative restrictions. The best REC business model is then selected based on a SWOT analysis, complemented with a detailed techno-economic analysis of the foreseen investments. According to the design calculations, the optimal PV plants/business-model option that emerged may achieve impressive environmental and economic benefits, i.e., reducing the municipality’s annual electricity-induced CO₂ emissions and electricity expenses by at least 68.40% and 594,461.54 €, respectively.

According to the latest energy efficiency European directive (EED 2023/1791/EU), the expected energy renovation rate of at least 3% of the buildings’ floor area each year towards nearly zero-energy buildings (nZEBs) is extended to include public buildings not only of the central government (as per the first EED 2012/27/EU) but also of regional and local authorities. This poses a great challenge, especially for Municipalities that often manage large building stocks with high energy demands. In response to this challenge, this paper presents the application of the so-called “typology approach” for conducting public buildings’ energy renovation plans at the local level. A computational survey is initially introduced to decide the optimal set of building-stock clustering criteria among all possible combinations, involving the minimization of the RMSE index regarding the primary energy consumption of each building. For a representative building from each identified typology, the key performance indicators (KPIs) are computed for alternative energy-upgrading scenarios. Exploiting the IMPULSE Interreg-MED project tools, the KPIs from each representative building are at first extrapolated to all buildings of the examined stock and, finally, a gradual energy renovation plan is automatically produced based on user-defined decision parameters including the required annual renovation rate. The methodology is applied for the case of the Municipality of Hersonissos in Greece. For the specific 44-buildings’ stock it was found that the optimal clustering set included four criteria, building use, construction year, heating, and a cooling system, leading to 15 building typologies. Finally, assuming a 7% renovation rate per year, a 12-year gradual renovation (nZEB transformation) plan is obtained foreseeing an 85% CO₂ emissions’ reduction.

Building electric energy is characterized by a significant increase in its uses (e.g., vehicle charging), a rapidly declining cost of all related data collection, and a proliferation of smart grid concepts, including diverse and flexible electricity pricing schemes. Not surprisingly, an increased number of approaches have been proposed for its modeling and forecasting. In this work, we place our emphasis on three forecasting-related issues. First, we look at the forecasting explainability, that is, the ability to understand and explain to the user what shapes the forecast. To this extent, we rely on concepts and approaches that are inherently explainable, such as the evolutionary approach of genetic programming (GP) and its associated symbolic expressions, as well as the so-called SHAP (SHapley Additive eXplanations) values, which is a well-established model agnostic approach for explainability, especially in terms of feature importance. Second, we investigate the impact of the training timeframe on the forecasting accuracy; this is driven by the realization that fast training would allow for faster deployment of forecasting in real-life solutions. And third, we explore the concept of counterfactual analysis on actionable features, that is, features that the user can really act upon and which therefore present an inherent advantage when it comes to decision support. We have found that SHAP values can provide important insights into the model explainability. In our analysis, GP models demonstrated superior performance compared to neural network-based models (with a 20–30% reduction in Root Mean Square Error (RMSE)) and time series models (with a 20–40% lower RMSE), but a rather questionable potential to produce crisp and insightful symbolic expressions, allowing a better insight into the model performance. We have also found and reported here on an important potential, especially for practical, decision support, of counterfactuals built on actionable features, and short training timeframes.

In current times, sustainability is paramount, and businesses are increasingly adopting renewable energy sources (RESs) and electric vehicle (EV) charging infrastructure to minimise their environmental impact and operational costs. Such a transition can prove challenging to multi-location businesses since each chain store functions under different constraints; therefore, the implementation of a corporate policy requires adaptations. The increased electricity demand associated with EV charging stations and their installation cost could prove to be a significant financial burden. Therefore, this study aims to investigate and develop strategies for effectively incorporating RES and EV charging stations into the operations of a supermarket chain in Crete. Monthly electricity consumption data, parking availability, and premise dimensions were collected for 20 supermarkets under the same brand. To achieve a more tailored approach to custom energy system sizing, the integration of energy storage coupled with a photovoltaic (PV) system was investigated, using the Moth–Flame Optimiser (MFO) to maximise the Net Present Value (NPV) of 20 years. The algorithm managed to locate optimal solutions that yield profitable installations for all supermarkets by installing the necessary number of PV units. Manual exploration around the solutions led to the optimal integration of energy storage systems with a total upfront cost of EUR 856,477.00 and a total profit for the entire brand equal to EUR 6,426,355.14.

This study explores the potential of ECs as a conduit for achieving a region’s or a country’s energy goals. The study focuses on Greece, where roughly 1700 energy communities have been founded since 2018. The methodology adopted is based, initially, on an extensive literature survey, aiming to outline the general energy goals on a regional and national level. On a second stage, focused interviews were accomplished with four of the biggest energy communities in Greece, investigating essential topics, such as their motivations, their business models, the obstacles they have faced, and their achievements. Environmental, economic, and energy security reasons were revealed as the main incentives for the foundation of energy communities in Greece. The major obstacles underlined by the interviewees were the bureaucracy and the changing, often towards a less supportive direction, legal framework. The contribution to a more sustainable energy environment, the reduction of the electricity procurement cost, and the remedy of energy poverty feature as the most important achievements. In the context of the ongoing energy transition in Greece, this article concludes that even though ECs can promote energy transition and mobilise a commonly acknowledged dialogue that can aid a nation’s efforts to achieve its energy goals, further investigation is required regarding the proposed policy initiatives, focused on strategies for upscaling the impact of energy communities, thus enabling them to flourish further.