Nowadays with the growth of large-scale societal infrastructure systems, there has been significant research attention on improving efficiency, guaranteeing safety, reducing operational costs, and decreasing the carbon footprint of these systems. In particular, this thesis is focused on Human-Cyber-Physical Systems (H-CPS) (e.g. smart grid, electric transportation networks, autonomous driving). An H-CPS is any physical system in which a mechanism is controlled by both computer-based algorithms and human inputs. With the increasing complexity of human-machine interfaces, the traditional engineering and operating strategies are not adequate to manage. In fact, a mix of tools from stochastic control, distributed optimization, machine learning, and game theory is required. For example, in modern electric transportation systems, without appropriate demand management and coordination schemes, Electric Vehicle (EV) charging patterns could create problems for power transmission and distribution networks, and hence reduce the environmental benefits of transportation electrification.
Furthermore, when managing demand to reduce costs in a power system, it is necessary to ensure that the operating constraints of the power grid are not violated as a result of our actions. Additionally, because of the availability of real-time data from these infrastructure systems, training a large-scale model over a vast amount of data requires sophisticated techniques that accelerate the training of learning models. Therefore, it becomes important to develop algorithms that are computationally efficient and consider the critical safety requirements of these systems.
The aforementioned problems are characterized by many challenges including: How can we encourage customers to act in a way that is more likely to benefit society even when it may conflict with their own interests? How do we make sure that the infrastructure systems' safety criteria are not disregarded while we are learning the proper procedures to optimize customer behavior? How do we make sure that our proposed algorithms are computationally efficient?
This thesis is focused on developing optimization and machine learning frameworks that promote efficiency and flexibility in large-scale societal infrastructure systems with the active involvement of humans. In the first part of the thesis, we focus on designing optimal price and routing mechanisms for a public charging stations network in electric transportation systems to coordinate customers (i.e., EV drives) towards a socially optimal behavior given their heterogeneity. In the second part of the thesis, we provide two theoretical learning guarantees for online decision-making problems in safety-constrained unknown linear systems. Moving on to the third part, we develop two methods to speed up the learning process of the online learning algorithms in new tasks based on their limited past experience with unknown linear systems. We also support our theoretical results in all three parts by significant improvement in numerical experiments.
