Local Renewable Energy Communities: Classification and Sizing

The transition from the current energy architecture to a new model is evident and inevitable. coming future promises innovative increasingly rigorous projects challenges for everyone involved in this value chain. Technological developments have allowed emergence of concepts, such as renewable communities, decentralized production, even storage. These factors incited consumers play more active role electricity sector contribute considerably achievement environmental objectives. With introduction need develop management optimization tools, mainly generation load management, arises. Thus, paper proposes platform capable clustering prosumers according their geographical characteristics create communities. Moreover, through platform, identification (homogeneous mixed self-sufficient communities) size each community are also obtained. Three algorithms considered achieve purpose: K-means, density-based spatial applications with noise, linkage (single-link, complete-link, average-link, Wards’ method). work, it possible verify algorithm’s behavior effectiveness players into A total 233 members 9 cities northern region Portugal (Porto District) were demonstrate application proposed platform. results that presented best classification performance, achieving 0.631 by complete-ink Silhouette score, 2124.174 Ward’s method Calinski-Harabasz index, 0.329 single-link on Davies-Bouldin index. Additionally, developed demonstrated adequacy, versatility, robustness concerning sizing