UC Berkeley

Key to a sustainable future is the transformative human use of energy and transportation--from a fossil-fuel dominant to a renewables-mixed portfolio of energy production, from a supply-follow-demand to a demand-responsive pattern of power consumption, and from capacity-oriented to usage-based allocation of logistics mobility. Motivated by these trends, my dissertation presents three essays to address the challenges that governments and businesses worldwide face in, respectively, 1) planning infrastructure for wind energy production, 2) designing coordination strategies for large-scale charging of plug-in electric vehicles, and 3) evaluating the economic and environmental viability of using shared mobility for retail e-commerce. Two common threads underlying my dissertation research are 1) strategic decision-making with insights into the operational level and 2) network optimization that takes into account the interdependencies both within and out of system boundaries. Solving these network optimization problems invokes the techniques of mixed-integer conic programming, decomposition algorithms, and continuous approximation modeling. Specifically, the three essays are organized as three chapters of the dissertation:

Chapter 1 studies the problem of jointly planning energy storage (ES) and transmission for wind energy generation. Regions with abundant wind resources usually have no ready access to the existing electric grid. However, building transmission lines that instantaneously deliver all geographically distributed wind energy can be costly. Energy storage systems can help reduce the cost of bridging wind farms and grids, and can mitigate the intermittency of wind outputs. We propose models of transmission network planning with colocation of ES systems. Our models determine the sizes and sites of ES systems as well as the associated topology and capacity of the transmission network under the feed-in-tariff policy instrument. We first formulate a location model as a mixed-integer second-order-conic program to solve for the ES-transmission network design with uncapacitated storage. Then we propose a method to choose ES sizes by deriving a closed-form upper bound. The major insight is that, in most cases, using even small-sized ES systems can significantly reduce the total expected cost, but their marginal values diminish faster than those of the transmission lines as their capacities expand. Despite uncertainties in climate, technologies, and construction costs, the cost-efficient infrastructure layout is remarkably robust. We also identify the major bottleneck cost factors for different forms of ES technologies.

Chapter 2 presents a hierarchical optimal control framework to coordinate the charging of plug-in electric vehicles in multifamily dwellings. A particular scenario is considered where distributed urban residential communities access electric power supplies through a common primary distribution transformer. We first formulate a centralized finite-horizon control problem. The proposed multistage mixed integer program seeks to maximize the total utility of the charging service provider while satisfying customers' charge demands and transformer capacity constraints. By exploiting the structure of the centralized model, we decompose the centralized problem with respect to each parking deck, based on the Lagrangian relaxation method; we design an effective heuristic method to find feasible solutions to speed up convergence. Case studies on operations of five parking decks following different charging strategies are carried out. Simulation results demonstrate that the proposed distributed hierarchical charging strategy outperforms the centralized charging strategy from the perspective of computational requirements. System reliability and customer privacy protection are also discussed.

Chapter 3 studies an integrated logistics system with shared mobility for retail e-commerce. Two socioeconomic transformations, namely, the booms in sharing economy and retail e-commerce, lead to the prospect where shared mobility of passenger cars prevails throughout urban areas for home delivery services. Local governments and logistics services providers are in need of evaluating the potentially substantial impacts of this mode shift, given their societal and environmental concerns and economic objectives. We addresses this need by providing a logistics planning framework: 1) Part one presents logistics planning models. These models characterize optimal routes of short-haul trucks and passenger cars, and generate the optimal density of service zones within which passenger vehicles pick up goods and fulfill the last-mile delivery. 2) Part two, based on empirical estimates, analyzes operating costs and greenhouse gas emissions implications of this sharing logistics paradigm. The findings suggest that a transition to this paradigm has the potential for creating considerable economic and environmental benefits, although immediate savings are not as achievable as one may conjecture. If being in this paradigm, even exclusively minimizing operating costs does not significantly increase emissions relative to the minimum level of emissions. A non-linear payment scheme can be used to efficiently induce shared mobility into passenger car home delivery services.