Efficient electricity systems under high penetration of renewable and distributed resources
- The electricity sector is in a period of major transition, driven by rapid renewable energy deployment (notably wind and solar), decommissioning of fossil fuel-powered generation, increased electrification of energy services, and technological development in distributed energy resources. The research presented here collects insights from four research studies addressing issues in electricity market design and demand side flexibility in the context of a changing electricity system. In a first study I quantify the economic and environmental impacts associated with a major change in the Texas electricity market. I show how under the zonal pricing regime, the discrepancy between the model used by the market and physical reality provided incentives for participants to take advantage of this difference and resulted in higher cost market outcomes. I estimate that daily operating costs of thermal generation given the same level of daily output fell by 3.9% with the implementation of the nodal market design. In contrast, I find that total heat input and CO2 emissions increased with the market design change. I investigate how changes in the operation of coal and natural gas technologies contributed to these outcomes, and find that a large proportion of the daily operating cost savings was due to the synergies achieved through increased efficiency of operation of these two generation technologies. In a second study I introduce a data-driven method to compute locational pricing in electricity distribution systems that reflects physical network constraints. I identify two key differences between locational pricing of the transmission network and locational pricing of the distribution network. I discuss how the proposed two-settlement locational pricing mechanism can provide appropriate incentives for demand flexibility to improve the efficient operation and investment in distribution networks. I then provide an illustrative example to demonstrate how this distribution network pricing mechanism provides incentives for improved efficiency of distribution network operation. In the next study, I discuss a study estimating demand flexibility potential in a large group of co-located commercial buildings in a district energy system. I develop a general methodology to estimate the aggregate cooling demand response of the buildings to thermostat temperature set point increases using empirically estimated building-level demand reductions in a subset of these buildings and observable characteristics of all buildings in the system. I apply the method to the Stanford University district energy system that is roughly equivalent to a city of 30,000 people. I estimate a 13.5% reduction in peak daily cooling demand under a 1.1 degree C daily set point increase in all campus buildings. On the highest demand day of 2020, I find that the predicted demand reduction could provide services equivalent to those provided by a lithium-ion battery with $4.6 - $8.0 million installation cost at current prices. In a final study, I further investigate the operations of the central energy facility of the district energy system at the Stanford University campus through an hourly simulation model. In this work I use a simulator of the district energy system under study to investigate how demand reductions from thermostat set point increases can enable lower installed capacity requirements to serve cooling demand during high demand periods due to extreme weather events. By simulating a heat wave during August of 2020, I show that demand response can significantly reduce cold water chilling and storage capacity needs.
|Type of resource
|electronic resource; remote; computer; online resource
|1 online resource.
|Triolo, Ryan Carter
|Degree committee member
|Degree committee member
|Stanford University, School of Engineering
|Stanford University, Civil & Environmental Engineering Department
|Statement of responsibility
|Ryan C. Triolo.
|Submitted to the Civil & Environmental Engineering Department.
|Thesis Ph.D. Stanford University 2023.
- © 2023 by Ryan Carter Triolo
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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