In view of the time-space uncertainty of electric vehicle charging demand, how to participate in the electricity market and how to maximize the operating profit has become a problem that needs to be solved. Firstly, this study established an electric vehicle travel prediction model based on multi-layer deep learning algorithm. The multilayer perceptron and long short-term memory network were used to learn the travel data and road condition data of electric vehicles, and the travel behavior and road condition of the next day were predicted by the trained prediction model. Secondly, considering the influence of the variability of road conditions on the prediction accuracy, the future path rolling optimization method and the speed-energy consumption model were used to simulate the travel behavior of electric vehicles the next day, so as to obtain more accurate time-space state and charge state of electric vehicles. Finally, considering the coordinated scheduling of the energy market, the charging and discharging behavior of electric vehicles in different periods was planned through the charging and discharging scheduling model of the day-ahead market to maximize the interests of electric vehicle agents. In order to prove the accuracy of the proposed prediction method, it was compared with the commonly used Monte Carlo method and Latin hypercube method. The results show that the deep learning algorithm proposed in this study has higher accuracy. The model was applied to the IEEE33 node test system for verification. The experimental results show that the peak-valley difference of the power system can be effectively reduced under the scheduling of electric vehicle agents. In the case of system congestion, the problem of system line congestion can be alleviated by changing the scheduling strategy of electric vehicles. The agent's revenue and the user's travel cost were analyzed. The results show that under the agent's scheduling, it can not only increase the income of agents, but also reduce the travel cost of users, and achieve a win-win situation.

As an important means of mobilizing demand-side resources, peer-to-peer (P2P) energy trading has drawn more and more attention from scholars. This paper constructs a P2P energy trading framework considering prosumers’ trading partner preferences (TPPs) and system risk. At first, we build the P2P trading models of prosumers equipped with different distributed energy resources (DERs), and TPP models. Secondly, to solve the established energy trading problem, a fully distributed double-consensus alternating direction method of multipliers (DC-ADMM) is proposed, which can achieve transaction consensus when considering market players’ TPPs. Then, a risk-based security constrained economic dispatch (RB-SCED) model based on AC power flow is established for the first time, by which a distribution system operator (DSO) checks system security and obtains risk-based locational marginal prices (RLMPs). Moreover, double-regulated price signals related to RLMPs which contain grid utilization prices (GUPs) and DSO’s retail prices realize management of players’ transactions. In the end, the proposed method is applied to an IEEE33 bus distribution system. Results show the proposed method effectively reduces system risk and ensures secure operation of system without direct management.