Spatial Distribution of Electricity-Related Data Value Indices in Transportation Networks

With the penetration of electric vehicles (EVs), the coupling of power systems and transportation networks is a widely-studied topic in recent years. As the insight of the role of data in power systems becomes increasingly profound, the potential of electricity-related data value indices being used in analysis of transportation networks' characteristics should be realized. Based on the research on data value based on the electricity transactions, the electricity-related data value, which indicates the improvement of the electric load forecast accuracy caused by actual data, is introduced into the scenario of transportation in this paper. Considering the traffic flow data in transportation networks can imply the electricity loads of electric vehicle charging stations, electricity-related data value indices are defined based on the traffic assignment problem (TAP). Obviously, different traffic flow data will affect electric loads of different electric vehicle charging stations, thus the spatial distribution of electricity-related data value indices in transportation networks can be entirely depicted according to the results of electric load forecast through a neural network. Finally, the topological structure and actual traffic data of Jiading District, Shanghai, China obtained from Shanghai Electric Vehicle Public Data Collecting, Monitoring and Research Center are applied for practical case studies. The electricity-related data value is validated to conform to the theoretical analysis and parameter setting, and the spatial distribution of electricity-related data value indices in transportation networks may contribute to improve power system operation by using actual traffic data.