Optimization-based modeling methods for reliable low carbon electricity portfolios [electronic resource]

While the potential of renewable energy resources to supply large portions of the United States energy demand has been demonstrated in resource assessments, the variability and uncertainty in renewable resource availability is anticipated to pose technological challenges to large-scale grid integration. This dissertation focuses on the effects of resource intermittency on renewable portfolio performance, particularly for systems with very high penetrations of renewables, in which today's operational heuristics and rules of thumb no-longer apply. We present a renewable portfolio planning tool that designs low cost and low carbon renewable portfolios and utilizes Monte Carlo methods to simulate system operation. The model simulates power output from wind turbines, concentrating solar power plants, rooftop photovoltaics, geothermal plants, hydroelectric plants, and natural gas turbines, while treating resource and demand forecast errors, forced outages, spinning reserves, and a reliability constraint. The model was applied to the California ISO operating area in order to identify a portfolio capable of reliably meeting the 2005-06 demand with an 80% reduction in operational carbon dioxide emissions. The model was also used to investigate several renewable deployment scenarios in order to develop useful parameterizations for portfolio performance as functions of the renewable fleet installed capacities. At low to moderate penetrations, renewable portfolio performance can be predicted by the expected capacity factor. However, at very high penetrations, renewable portfolio performance is depressed by both the need to curtail in hours when renewable power exceeds the demand for electricity and an increasing need for spinning reserves. Complete decarbonization of the closed system under study is found to rely on the deployment of energy storage fleets large enough to decouple in time the availability of renewable power and the demand for electricity. Preliminary results from quasi-stochastic portfolio planning simulations suggest that the competitiveness of energy storage will initially be driven by its ability to provide zero-emissions reserves. Furthermore, it is concluded that for fully decarbonized portfolios, future modeling efforts should focus on the appropriate treatment of longer term uncertainty in renewable resource availability and the effects of information-limited dispatch decisions on optimal planning. The modeling work described in this dissertation also suggests that achieving very high penetrations of renewables will rely on: improved conventional fleet and demand-side flexibility; the inclusion of curtailment controls in PV inverters; new market designs that fully capture the values of online reserve capacity and renewable curtailment; significant investments in transmission and distribution infrastructure; and new communications systems between renewable facilities and intermittency-mitigating technologies like energy storage and demand response systems.

Advisor: Jacobson Mark Z. (Mark Zachary) primary advisor. Thesis advisor
Gerritsen Margot (Margot G.) advisor. Thesis advisor
Weyant John P. (John Peter) advisor. Thesis advisor