Distributed collaborative optimization for coupled transportation and power systems operation considering carbon emission and elastic travel demand

With the continuous expansion of the scale of electric vehicles (EV), the coupling between the distribution system and the urban transportation system is deepening. In the context of global decarbonization, it is necessary to formulate reasonable incentives to optimize the operation of coupled transportation and power systems towards an economical and low-carbon system. To achieve this goal, we propose a collaborative optimization model that considers the overall carbon emission cost of the coupled system. To respect the actual living habits of users, elastic travel needs are also considered. First, on the transportation system side, considering two main participants, electric vehicles and gasoline vehicles (GV), a game-theoretic based traffic assignment model using user equilibrium criteria is built. To reduce the computational complexity of the sub-problem in achieving the user equilibrium, an efficient path generation model is designed, which considers the charging decision and range anxiety problems of electric vehicles and the cost of carbon emissions of gasoline vehicles. Second, on the power grid side, the optimal power flow using second-order cone programming (SOCP) for power distribution systems is built, considering the cost and carbon emission of various energy sources. Third, considering the incomplete information between the coupled system, the entire model is decoupled and solved by a distributed collaborative optimization model using the alternating direction method of multipliers (ADMM) algorithm. Finally, the proposed model and solution methods are verified by comprehensive case studies. Simulation results show that the distributed collaborative optimization model can effectively reduce the emissions of the coupled system, and provide a scientific basis for the formulation of charging prices.