## 3 The Results
### Results of the Literature Review  
For its meticulousness in analysis and literature mapping, the software chosen for bibliometric analysis is CiteSpace 6.3. R3 (64 bit) Advanced(r) [24], an open-source Java-based application. To correctly interpret the data generated by the bibliometric analysis software, the following key evaluation parameters are provided:  
* _the centrality of the interactions (w)_; according to graph theory, it is an indicator used to assess the importance of nodes within a network [25]; it measures how often a node acts as an intermediary between two other nodes in the network [16];
* _burst strength (BS)_; this is an indicator that quantifies the citation burst, which refers to the time window during which an author or paper suddenly experiences a sharp increase in citations [26];
* _mean silhouette (S) and modularity (Q)_; these are used to measure the overall structural properties of the network. The results obtained from the bibliometric analysis of the selected literature are presented and discussed below, with reference to the presentation of clusters and the citation burst detected from the analysis of keywords. The discussion focuses on the main trends and challenges in the field of V2G that have emerged from the classification of clusters, with the aim of providing a comprehensive overview of the key attributes and variables essential for understanding and modeling the charging and usage behaviors of EVs in a V2G context, as they directly influence the ability to respond to dynamic energy demand and maximize benefits for both users and the electrical system. The process of cluster creation is a crucial phase for the analysis of citation networks, as it allows for the identification and understanding of the underlying structures within  
Figure 2: Reviewed paper per year. a research field. Initially, the full library is loaded and saved in a reference directory to be used later as input for a newly created project. The dataset has to contain sufficient information, such as citations and references, to enable CiteSpace 6.3. R3 (64 bit) Advanced(r) to build a meaningful network. The citation information within the dataset was then used to build a network where the nodes represent the reviewed studies and the connections represent the citations between these documents. The connections between the nodes are often based on co-citations, which indicate when two studies are cited together in one or more subsequent documents. Once the network is built, clustering algorithms are implemented to identify groups of nodes (documents) that are strongly interconnected. Each cluster represents a group of documents that share a common theme, a research line, or a similar topic. After identification, the clusters need to be interpreted and characterized. There are different approaches to use to identify the representative labels for each group. Labels can be based on noun phrases extracted from the titles of articles, their abstracts, or keyword lists. The algorithms mostly used for this purpose include Latent Semantic Indexing (LSI) and Log-Likelihood Ratio (LLR). The latter is often preferred for its ability to identify unique and representative labels [24]. In addition, it is useful to explore the quality of homogeneity within each cluster. It can be performed through different metrics, such as modularity (\(Q\)) and silhouette values (\(S\)). Therefore, they are used to measure the internal cohesion and separation between clusters. A high modularity value indicates that the network is well divided into distinct clusters, while a high silhouette value suggests that nodes within a cluster are strongly correlated with each other. Finally, the clusters and their connections are visualized on a network map. This visualization allows researchers to explore the clusters, see how they are interconnected, and interpret the citation dynamics and research trends within the field of study.