### Significance of Work  
During this research, an exhaustive literature review was undertaken to assess the state of LEMs within the CEM. Figure 3 shows the network visualization mapping generated in VOS Viewer to represent the relationships between different concepts or terms based on their co-occurrence in the data or in the literature review that was carried out. In  
Figure 2: Representation of wholesale and retail electricity markets. this network visualization map, each node is denoted by a colored node and represents a specific term or concept related to energy markets and technologies such as \"demand response\", \"P2P trading\", and \"reinforcement learning\", while the frequency of the research item's usage determines the node size. The higher the frequency of item usage, the larger the item's label. The lines (edges) connecting the nodes indicate the relationships between terms. Thicker lines or closer proximity generally signify stronger relationships or higher co-occurrence. The keyword co-occurrence network analysis signifies the scientific footsteps and trends that have progressed over time, sketches the conceptual structure, and summarizes it in a bibliographic collection to map and cluster the terms extracted from the keywords [16]. As can also be observed, nodes like \"demand response\" and \"transactive energy\" are centrally located, suggesting they are key terms with many connections to other terms, indicating their importance in the network. The \"Demand Response\" node is central, connecting to various other concepts, showing its pivotal role in energy management and smart grid systems, where the \"Transactive Energy\" node links heavily to concepts in the red cluster, emphasizing its relevance in modern energy trading systems involving blockchain and prosumers. The \"Reinforcement Learning\" node located in the blue cluster is associated with LEM and P2P trading, highlighting its emerging role in adaptive and intelligent energy management systems. The \"Energy Market Design\" node represents the market design, distributed energy resources, and storage, indicating ongoing research and development in optimizing energy systems for efficiency and resilience, whereas \"Smart Grids and Microgrids\" defines the connections between \"smart grid\", \"microgrid\", and \"distributed energy resources\", highlighting the integration of localized and distributed systems in modern electricity grids. In addition, \"Blockchain and Energy Trading\" characterize the relationship between \"blockchain\" and \"energy trading\", and \"P2P\" indicates the innovative approaches being explored for decentralized and secure energy transactions. From the reviewed literature and network visualization mapping, two distinct market types can be identified: large-scale energy markets and local energy markets. The large-scale energy markets encompass geographic considerations at the scale of countries, multiple countries, or even the global stage. In contrast, the LEM focuses on smaller geo-graphic regions, including cities, specific regions, or residential communities. In this work, both large-scale and local energy markets are studied. Large-scale energy markets are essential when studying local markets because the two are deeply interconnected but still address different aspects of energy systems. Local markets operate within broader systems where energy flows, pricing, and policies are influenced by national or regional dynamics. Large-scale markets impact local pricing, renewable energy integration,  
Figure 3: Network visualization mapping of research trends in energy market. and grid stability while providing critical context for regulatory compliance and investment planning. Understanding these larger systems helps ensure that local market strategies are resilient, aligned with broader trends, and capable of adapting to external influences, such as shifts in supply-demand balance or policy changes. Figure 4 summarizes the number of papers reviewed in terms of the type of market.