| Author: | Jin, Ziliang |
| Title: | Data-driven smart city operations: bridging shared mobility and electrical power grid |
| Advisors: | Pan, Kai (LMS) |
| Degree: | Ph.D. |
| Year: | 2024 |
| Subject: | Smart power grids Smart cities Hong Kong Polytechnic University -- Dissertations |
| Department: | Department of Logistics and Maritime Studies |
| Pages: | xi, 212 pages : color illustrations |
| Language: | English |
| Abstract: | Smart city operations focus on integrating modern technologies and collected data to enhance the quality, efficiency, and sustainability of life for inhabitants in cities. In this thesis, we present three recent studies on the shared mobility and electrical power grid, which are at the crux of smart city operations. Through these studies, we hope to address some challenges in transportation and sustainability in urban areas. In the first study, we consider two shared micromobility firms competing in the same service area, each providing micromobility vehicles (e.g., bikes and scooters) to satisfy uncertain demands. Each firm solves an integrated vehicle allocation and relocation problem, in which the total number of vehicles allocated by the two firms together in each service region is restricted by the city regulator, and provides a Nash equilibrium. Each firm’s decision-making problem is formulated as a two-stage stochastic program on a spatial-temporal network, with the objective of maximizing her expected profit. To improve firms’ operations with the limited number of allocated vehicles, we propose an innovative capacity-sharing agreement, under which a firm can share spare capacity for a fee with her opponent. Extensive numerical experiments based on real data reveal that regulator restrictions impact firms’ profitability and service level. Capacity sharing can reduce the number of relocated vehicles and also improve firms’ profitability. To promote the capacity-sharing agreement, the regulator should provide incentives to the firms. In the second study, we focus on a shared mobility system with electrical vehicles (EVs). Unlike micromobility vehicles, EVs face additional challenges in managing their battery levels. They must charge adequately to meet trip demands and can discharge electricity to the power grid through vehicle-to-grid (V2G) technology to earn revenue. We frame the operator’s EV planning and operation under correlated uncertainties as a two-stage distributionally robust optimization (DRO) problem. To increase computational efficiency, we propose inner and outer approximations for the DRO problem and develop an algorithmic approach incorporating time-based decomposition techniques. Numerical results based on real data suggest that EVs majorly charge electricity during the early hours when electricity prices and consumer trips are low. Conversely, they discharge electricity when prices are high. Faster charging decreases the number of allocated vehicles, improves the vehicle utilization rate, and reduces total costs. Comparing two pricing schemes for charging EVs, electricity-based and time-based, we observe more frequent charging and discharging under the time-based scheme than the electricity-based scheme. In the third study, we explore a grid-vehicle integration (GVI) system employing V2G while managing uncertainties in renewable generation, power load, and EV trip demand. We formulate the problem of operating a GVI system as a two-stage robust mixed-integer program. In the first stage, the grid operator, aiming to minimize the worst-case total cost, decides whether to start up a generator. In the second stage, the grid decides power generation levels and charging/discharging interactions with EVs to satisfy the power load. Meanwhile, the mobility operator utilizes the EV fleet to fulfill the interactions and satisfy EV trip demands. To address a significant computational challenge, we propose a machine learning-driven optimization approach. Our approach outperforms a commercial solver in both computational time and solution quality for large-scale instances based on real data. Out-of-sample tests reveal that V2G can reduce the number of required generators and stabilize power generation by “filling” the low power load and “shaving” the peak power load. Moreover, V2G helps achieve a substantial reduction in carbon emissions compared to the case without V2G. The impact of V2G is more pronounced under a bimodal power load pattern than a unimodal pattern. We also find that achieving carbon neutrality in this integration system is feasible yet challenging. |
| Rights: | All rights reserved |
| Access: | open access |
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