Holistic management of mobile energy storage resources in coupled power and transportation systems

• Main Author: Liu, Xiaochuan
• Other Authors: Soh Cheong Boon
• Format: Thesis-Doctor of Philosophy
• Language: English
• Published: Nanyang Technological University 2021
• Subjects: Engineering::Electrical and electronic engineering::Electric power::Auxiliaries, applications and electric industries; Engineering::Electrical and electronic engineering::Electric power::Production, transmission and distribution
• Online Access: https://hdl.handle.net/10356/150274
• Institution: Nanyang Technological University

In the face of future energy and environmental challenges, huge growths in transportation as well as its electrification have been witnessed in recent decades, which has brought a large number of potential mobile energy storage resources. Besides the electric vehicles (EVs) under plug-in charging mode, which have been extensively studied in the literature, newly emerging types of mobile energy storage resources such as the EV batteries under battery leasing and swapping mode, and truck-mounted mobile energy storage systems (MESSs) can also bring potential opportunities to further improve the reliability, flexibility and efficiency of future smart grids. However, as these emerging mobile energy storage resources also bring close couplings and interdependences between power and transportation systems, research gaps still exist in terms of mathematical modeling and management scheme designs, which makes these emerging resources generally underutilized. In order to achieve the coordination between these mobile energy storage resources and power systems, as well as the full utilization of their temporal-spatial mobility while considering certain transportation system characteristics, novel models and management schemes are urgently needed. To achieve the full utilization of the EV battery mobility under battery leasing and swapping mode while ensuring uninterrupted battery swapping services, and to fill in the gaps in the modeling and management of the coordinated operation between battery charging stations (BCSs) and battery swapping stations (BSSs), this thesis first proposes a novel closed-loop supply chain (CLSC) based battery swapping-charging system (BSCS) model. A novel battery logistics model based on the time-space network (TSN) technique is established to describe and utilize the temporal-spatial mobility of the EV batteries. The charging and discharging of the batteries in the BCSs and the battery logistics are optimally managed to maximize the revenue of the BSCS while satisfying the battery demand for battery swapping service at each BSS. A heuristic method based on a randomly permuted alternating direction method of multipliers (RP-ADMM) is adopted to solve the optimization problem in a more efficient and distributed way. Simulation results verify the feasibility of the proposed model and the heuristic solution with small optimality losses and less computation time. To take into account the large-scale deployments of BSCSs in the future, this thesis further extends the single BSCS model to a multi-region battery swapping and charging network (MBSCN) model to achieve efficient and collaborative management of the BSCSs located in different regions with different EV user behavior and local system features. A novel multilayer TSN-based network-wise battery logistics model is proposed to manage both the intra-BSCS and interregional battery logistics more efficiently, which also enables the sharing of the mobile energy storage resources within different BSCSs. A distributionally robust chance-constrained service model is established to address the regional battery demand uncertainties without requiring assumptions on the probability distributions or a large amount of historical data. The battery charging and discharging tasks are optimally allocated to each BSCS and the battery logistics are optimally managed according to the locational energy price and the battery demands. Simulation results are presented to verify that the proposed MBSCN model is more flexible and efficient when interregional battery exchanges are incorporated. For truck-mounted MESSs, to fully leverage their mobility for enhancing the operational flexibility of coupled distribution and transportation networks (CDTNs) while considering the system uncertainties from variable renewable energy (VRE) sources and the daily traffic demands in transportation networks, this thesis proposes a stochastic management scheme to achieve the coordination among MESSs, hybrid AC/DC microgrids (MGs) and CDTNs. A novel stochastic multi-layer multi-timescale TSN model that incorporates the hourly traffic user-equilibrium (UE) results obtained from the adopted traffic assignment problem (TAP) model is also proposed to facilitate the modeling and scheduling of different MESSs with various mobility features while considering the uncertainties in the traffic flows in the transportation network and the resulting congestion delays. The scheduling and management of MESSs and CDTNs are formulated as a two-stage stochastic optimization problem, where the uncertainties of traffic demands, VRE outputs and loads are depicted using scenarios. Case studies are performed to verify the effectiveness of the proposed two-stage stochastic management scheme and the UE-TSN model, as well as the effectiveness of MESSs to serve as mobile energy storage resources for MGs with mismatched generation and conversion capabilities.