Spatial Optimization Perspectives on Sustainable Energy Landscapes

The rapid economic growth worldwide has imposed much pressure on the energy sector for sustainable development. Although much effort has been made to achieve sustainability, the world is currently not on track to meet the main energy-related components of United Nation’s Sustainable Development Goals (SDGs). Great transformations are needed in the way we produce and consume energy. Focused on the two largest contributors to global energy-related greenhouse gas emissions, the electricity production sector and transportation sector, this dissertation presents three studies to help improve energy efficiency and promote the transition to more sustainable energy consumption. The first study (Chapter 2) provides a GIS-based method to evaluate the residential solar photovoltaic (PV) potential at the urban scale. High-resolution LiDAR data are used to more accurately characterize residential rooftops and the surrounding built environment. Annual meteorological data are incorporated to capture temporal variation. Optimal panel tilt angle and orientation are considered. A case study is conducted in the City of Tucson, Arizona, testing the validity of the method proposed. Building on the first study, the second study (Chapter 3) focuses on the spatial layout design of solar PV systems to optimize performance. Unlike studies that focus on an individual panel’s parameters such as tilt angle and orientation or solar cell materials, this study proposes a new spatial optimization model, the maximal PV panel coverage problem (MPPCP), to identify the best spatial arrangement of multiple solar PV panels. Different orientations and alignment scenarios are considered to achieve optimal positioning of solar panels while accounting for practical installation constraints. The new problem is applied to locate solar PV arrays on a rooftop. Model performance and computational efficiency are discussed. In the third study (Chapter 4), an approach to the strategic location of alternative fuel (alt-fuel) stations is proposed to help promote sustainable transportation. While a number of optimization models have been proposed for siting refueling/recharging stations for alt-fuel/electric vehicles, many of these approaches require detailed origin-destination (OD) data of refueling trips that are often very costly or challenging to obtain. Chapter 4 introduces two new arc-based location models for the early and later stages of alt-fuel station planning when OD data are unavailable or unsuitable. Station spacing parameters are proposed to reduce redundant coverage while helping fill in regional coverage gaps. The new models are applied to planning a network of compressed natural gas (CNG) fueling stations for heavy-duty CNG-powered trucking in the Southwest U.S. With a focus on spatial optimization methods, the three studies help assess energy potential and identify the best strategies to locate infrastructures for the production and consumption of more sustainable energies. In addition to the important applications, this dissertation also makes contributions to the spatial optimization field by developing new spatial optimization models that can be applied to other studies.Release after 08/20/202