Coordinated power and train transportation system with transportable battery-based energy storage and demand response: A multi-objective stochastic approach

Abstract

Due to the recent upward trend in utilizing renewable energies (REs) as a result of economic reasons and global trend in reducing greenhouse gases (GHG) emission, the independent system operators (ISOs) have been confronted with several obstacles. The system operators have to overcome not only the fluctuating nature of renewable generation, but also the transmission of large amounts of REs from plants to the load centers. This is because of avoiding network congestion to obtain full utilization of renewable generation capacity. In addition, the cleaner production of power systems is one of the major concerns of today’s world. Transportable battery-based energy storage (TBES) system is a novel idea, which utilizes rail roads as a complementary infrastructure to transmit electric power in the form of stored energy in batteries. In this paper, a multi-objective stochastic network constrained unit commitment (NCUC) problem with TBES and demand response (DR) program is presented to minimize the operation cost as well as the total GHG emission of the power grid. The ε-constrained approach is utilized to solve the multi-objective problem and the compromise solution for each case is defined by the min-max method. To manage the wind and load uncertainty the stochastic optimization approach is applied using Monte Carlo simulation method. Moreover, DR programs are introduced as one of the flexible resources to manage the uncertainties, provide cleaner operation and reduce the overall cost by modifying the load profile and peak load shaving. The proposed model is implemented on a 6-bus power system coordinated with 3-station railway network. Simulation results revealed that considering TBES and DR reduces the transmission congestion, total operation cost and GHG emission. The overall operation cost, without considering the emission constraints, is decremented by 11.2% in case of using TBES and DR. On the other side, applying emission constraints led to an increase in overall cost to obtain the compromise solution. However, the total GHG emission is reduced by 17.2% in case of developing TBES and DR.