Optimal Operation of Battery Swapping Stations for Flexible Electric Mobility in Distribution Systems

This doctoral thesis investigates the optimal operational strategies for battery swapping stations (BSSs) from various perspectives by developing advanced optimization frameworks. The research is structured around four distinct studies. The first study presents a comparative assessment of operational planning between BSSs and conventional charging stations (CSs). It develops two distinct mixed-integer linear programming (MILP) models, one for BSSs and one for CSs, ensuring consistency in their objective functions as well as in their technical, economic, and social parameters. These models aim to maximize profit by considering operational costs related to energy procurement, battery degradation, and quality-of-experience (QoE) penalties, while revenues arise from charging services and vehicle-to-grid (V2G) injections in CSs and from swapping services and battery-to-grid (B2G) injections in BSSs within the distribution system (DS). This comparative approach highlights the operational advantages of BSSs over traditional CS configurations. The second study introduces a novel hybrid station concept called Battery Charging and Swapping Station (BCSS), integrating both charging and swapping services within a unified infrastructure. The BCSS provides ancillary services to the DS, effectively leveraging multiple flexibility sources through battery-to-everything (B2X) and vehicle-to-everything (V2X) interactions. A comprehensive MILP model is developed to optimize station profitability by strategically managing revenues from EV service provision and energy transactions, including V2G and B2G operations under dynamic electricity pricing. Additionally, the model accounts explicitly for battery degradation costs and service penalties due to unmet EV requests, ensuring realistic economic assessments. The third study evaluates the role of BSSs in alleviating the negative impacts of uncontrolled electric vehicle (EV) loads on the DS. A mixed-integer quadratically constrained programming (MIQCP) model is developed based on an AC optimal power flow (OPF) framework. The objective function aims to minimize voltage deviations by accurately capturing nonlinear power flow dynamics while ensuring compliance with both operational constraints and user-specific requirements. This study also integrates cost minimization for load shedding and penalties related to QoE, thereby ensuring comprehensive system-level optimization. Finally, the fourth study addresses the incentivization of BSS aggregators to actively participate in congestion management scenarios arising from increasing EV penetration. The research employs a distribution locational marginal pricing (DLMP)-based bilevel optimization framework to concurrently represent the perspectives of both BSS aggregators and DS operators (DSOs). The upper-level optimization aims to maximize aggregator profits from energy service transactions, while the lower-level optimization focuses on minimizing the DSO’s energy procurement and load shedding costs using a second-order cone programming (SOCP)-based power flow formulation. Comprehensive congestion management strategies are implemented by jointly incorporating voltage stability constraints and line flow limitations. To achieve realistic operational outcomes, the model explicitly accounts for battery degradation, individual battery tracking, and QoE considerations.