## 4 Discussion and Perspectives  
Although smart grids are still in an early stage of development, in recent years, societal implementation has gained momentum through the deployment of smart meters and small and medium scale smart grid pilots [44; 58; 59; 60]. The transition to smart grids would create electricity systems that would enable consumers to make informed and empowered energy-related choices, promoting personal behavioral changes [39; 61]. In this regard, evaluative studies and reports, such as [62; 63; 64; 65] have highlighted the relevance of end-users in smart grid deployment. Nowadays, statistically significant data concerning social factors in stakeholder issues are few compared to the number of papers on technologies and market layers analyses [1]. Social-acceptance, in regard to product adaptation, appears to be a slow process, nevertheless, it is one of the key factors in the fast deployment of smart grids into the actual grid [39]. The continuous interactions and co-evolution of the three layers will define the smart grid environment, and the more interaction there is, the more rapid the transition will be to a fully smart grid. Our study aimed to highlight this multi-layer interaction to enable a wider point of view and practical solutions. The first crucial point for social implementation is the selection bias in monitoring surveys caused by the so-called Hawthorne effect (see [66]). For instance, one of the world's largest datasets of appliances, Pecan Street [67], is sharing the circuit-level residential electricity data in the USA intended for research purposes. The data has shown that the monitored residences are consuming far less electricity (33-60%) compared than the means of that specific region's other network users, probably due to being a volunteer or being conscious that they are participating in a pilot experiment. Active participation of the individuals is crucial for efficient integration and energy resilience, therefore the design of smart products and services is crucial to maintaining their motivation to be energy efficient. In the stakeholders layer, detailed consumer profiles analyses are needed in order to point out such important facts, which will guide the technologies and market layers on structural issues in the electricity network. Treating raw data according to only the market and technologies layers, without considering end-users and stakeholders, is already inducing unneglectable simulation uncertainties on an hourly basis, and the high prediction errors may induce significant organizational and structural errors. The data purged of bias will help us to focus on specific smart energy products and services, their research and development [68]. Pecan Street's recent data analyses from 12,083 monitored residences showed that 50% of the electricity used was related to air conditioning units, water heaters, and refrigerators [69]. Such applications could be shiftable loads if thermal isolation was feasible and sufficient, which again underlines the importance of load flexibility management. The main barrier would be the initial costs of these zero energy households, additionally equipping them with devices such as heat pumps, which in the EU are now supported by policy. Creating facilities and smart energy products, depending on end-users' surveys about these devices and comfort expectancies, would be a cost-effective solution. Moreover, users' knowledge with regard to smart grids (such as an abundance of feedback information) has been shown to both enhance acceptance and to create confusion, and in contexts, people prefer demand load control (the ability of energy suppliers to control user consumption) while they reject it in others. For EU countries, the risk of residential peak demand is high and similar for countries such as Italy, Norway, and Germany, for average households, while on the other hand, Bulgaria and the UK differ completely [70]. Global warming also plays a role in the trend of peak demand.