Grid-Interactive and Dual-Side Demand Responsive Timetable Scheduling Optimization for Flexible Urban Metro Systems

Metro systems, as major electricity consumers, establish a critical demand-side linkage between public transportation services and the utility power grid. With ongoing expansion, metro systems impose increasing challenges on electricity supply due to their concentrated energy demands, while also offering significant potential as controllable loads to enhance grid flexibility. To enhance grid flexibility while effectively meeting passenger demand, this paper proposes a novel dual-side demand-responsive and grid-interactive metro operation framework. This framework allows metro systems to support grid-side demand response by aligning operational schedules with grid conditions and passenger demand, without compromising service quality. A spatiotemporal model is established to estimate metro train power consumption under various speed profiles and real-time passenger loads. An adaptive iterative greedy algorithm is developed to coordinate metro operations in response to real-time power system conditions. Unlike global optimization approaches that suffer from exponential complexity, this algorithm employs a sequential and simulation-based decision process. It prioritizes real-time feasibility by making locally optimal dispatching decisions, thereby achieving a practical trade-off between solution optimality and computational speed. The algorithm offers high computational efficiency and dynamically optimizes train timetables and operational modes, thereby improving grid flexibility. Case studies using real-world metro data demonstrate that the proposed method achieves a 17.8% reduction in electricity costs and provides up to 6.56 MW of DR capacity without compromising passenger service. This study reveals the potential of metro systems as large-scale, dispatchable loads that contribute to the flexibility of utility grids.