A Review and Prospects of Research on Coordinated Optimization Operation of Energy Stations

With the rapid evolution of the global energy transition and the green transformation of the automotive industry, the charging demands for New Energy Vehicles (NEVs) are experiencing rapid growth and increasing diversification, posing significant challenges to the balance and stability of the energy network. As a form of green infrastructure, the Energy Station (ES) plays a pivotal role in enhancing energy system flexibility, promoting zero-emission and low-carbon transportation, and facilitating the integration of renewable energy. From the perspective of the ES industry's development, this paper presents a comprehensive review of ES research directions from four viewpoints: intra-station optimal configuration, station-vehicle coordinated scheduling, internal energy management, and inter-station transaction decision-making. This paper provides an in-depth analysis of the multiple, concurrent challenges faced by ES optimal operation, namely: the fusion of massive multi-modal data, the precise characterization of semantic demands, and the resolution of non-linear, non-convex optimization problems. It is further concluded that existing methods can only solve a specific one or a subset of the aforementioned problems, and a superior solution for addressing these simultaneously occurring issues is still lacking. In response, this paper proposes a technical solution for decision-making assisted by a Large Language Model (LLM) and constructs the 'Multi-modal Semantic Embedding and LLM Agent-based Coupled Reasoning' research framework. This framework is intended to holistically solve the developmental problems of the ES industry. Finally, this paper summarizes the limitations of LLM while providing an outlook on potential future research directions for ES.