PhD Thesis Defense: Nikko Bovornkeeratiroj, Accelerating Sustainability of the Electric Grid Using Distributed
Content
Speaker
Abstract
In recent years, the impacts of climate change have become more visible, raising concern and active movement in sustainability efforts. For instance, energy transition, which focuses on shifting from traditional fossil fuels towards renewable energy sources, is a critical strategy for mitigating the effects of climate change. The electrical grid is an important part of the energy transition since it still heavily relies on dirty sources such as coal, oil, and natural gas in many locations. Moreover, other sectors such as transportation, industry, and agriculture are transiting to all electric economy to reduce their emissions which leads to higher demand and, of course, emissions from the grid. The advances of the Internet of Things (IoT) and the proliferation of high-capacity networked energy devices at household-level, such as electric vehicles (EV), batteries, and heating, ventilation, and air conditioning (HVAC) have introduced opportunities for transforming electric demand at house-level and for coordinated control of those residential loads at a large scale. This provides a new and powerful form of demand response and new opportunities to accelerate sustainability of the grid.
This thesis puts forth a central focus on sustainability in electricity grids with the presence of distributed energy resources. At the same time, the study also takes a human-centric design approach which considers environmental, economical, convenient, and privacy aspects of electric consumers in the design of the systems and algorithms. To address those challenges, first, I propose a grid peak shaving framework that consists of peak prediction and a control algorithm that utilizes a distributed and heterogeneous pool of energy resources to perform flexible grid peak shaving. The algorithm can take home owner’s preference into consideration. Second, I examine electricity grid peak patterns, and then present peak prediction algorithms that can predict peak time of the day, peak day of the month, and peak day of the year respectively. I also provide reference datasets for peak forecasting in energy systems. Third, to prevent privacy leakage from the electric consumption data, I introduce an algorithm that shifts electricity demand with household batteries to prevent occupancy leakage while preserving other useful information. Finally, I analyze the potential of reducing grid’s carbon emissions with distributed energy resources and also propose a control algorithm that focuses on decarbonizing the grid while balancing the price tradeoff and respecting user and grid constraints.
Advisor
Prashant Shenoy