Capacity Investment in Renewable and Conventional Energy Sources

This dissertation studies capacity investments in energy sources, with a focus on renewable technologies, such as solar and wind energy. We develop analytical models to provide insights for policymakers and use real data from the state of Texas to corroborate our findings.

We first take a strategic perspective and focus on electricity pricing policies. Specifically, we investigate the capacity investments of a utility firm in renewable and conventional energy sources under flat and peak pricing policies. We consider generation patterns and intermittency of solar and wind energy in relation to the electricity demand throughout a day. We find that flat pricing leads to a higher investment level for solar energy and it can still lead to more investments in wind energy if considerable amount of wind energy is generated throughout the day.

In the second essay, we complement the first one by focusing on the problem of matching supply with demand in every operating period (e.g., every five minutes) from the perspective of a utility firm. We study the interaction between renewable and conventional sources with different levels of operational flexibility, i.e., the possibility

of quickly ramping energy output up or down. We show that operational flexibility determines these interactions: renewable and inflexible sources (e.g., nuclear energy) are substitutes, whereas renewable and flexible sources (e.g., natural gas) are complements.

In the final essay, rather than the capacity investments of the utility firms, we focus on the capacity investments of households in rooftop solar panels. We investigate whether or not these investments may cause a utility death spiral effect, which is a vicious circle of increased solar adoption and higher electricity prices. We observe that the current rate-of-return regulation may lead to a death spiral for utility firms. We show that one way to reverse the spiral effect is to allow the utility firms to maximize their profits by determining electricity prices.

The impact of geologic variability on capacity and cost estimates for storing CO 2 in deep-saline aquifers

While numerous studies find that deep-saline sandstone aquifers in the United States could store many decades worth of the nation's current annual CO 2 emissions, the likely cost of this storage (i.e. the cost of storage only and not capture and transport costs) has been harder to constrain. We use publicly available data of key reservoir properties to produce geo-referenced rasters of estimated storage capacity and cost for regions within 15 deep-saline sandstone aquifers in the United States. The rasters reveal the reservoir quality of these aquifers to be so variable that the cost estimates for storage span three orders of magnitude and average>$100/tonne CO 2. However, when the cost and corresponding capacity estimates in the rasters are assembled into a marginal abatement cost curve (MACC), we find that ~75% of the estimated storage capacity could be available for<$2/tonne. Furthermore, ~80% of the total estimated storage capacity in the rasters is concentrated within just two of the aquifers-the Frio Formation along the Texas Gulf Coast, and the Mt. Simon Formation in the Michigan Basin, which together make up only ~20% of the areas analyzed. While our assessment is not comprehensive, the results suggest there should be an abundance of low-cost storage for CO 2 in deep-saline aquifers, but a majority of this storage is likely to be concentrated within specific regions of a smaller number of these aquifers. © 2011 Elsevier B.V.

Biomimetic engineered muscle with capacity for vascular integration and functional maturation in vivo.

Tissue-engineered skeletal muscle can serve as a physiological model of natural muscle and a potential therapeutic vehicle for rapid repair of severe muscle loss and injury. Here, we describe a platform for engineering and testing highly functional biomimetic muscle tissues with a resident satellite cell niche and capacity for robust myogenesis and self-regeneration in vitro. Using a mouse dorsal window implantation model and transduction with fluorescent intracellular calcium indicator, GCaMP3, we nondestructively monitored, in real time, vascular integration and the functional state of engineered muscle in vivo. During a 2-wk period, implanted engineered muscle exhibited a steady ingrowth of blood-perfused microvasculature along with an increase in amplitude of calcium transients and force of contraction. We also demonstrated superior structural organization, vascularization, and contractile function of fully differentiated vs. undifferentiated engineered muscle implants. The described in vitro and in vivo models of biomimetic engineered muscle represent enabling technology for novel studies of skeletal muscle function and regeneration.

Neuronal Survival of the Fittest: The Importance of Aerobic Capacity in Exercise-Induced Neurogenesis and Cognition

It is commonly accepted that aerobic exercise increases hippocampal neurogenesis, learning and memory, as well as stress resiliency. However, human populations are widely variable in their inherent aerobic fitness as well as their capacity to show increased aerobic fitness following a period of regimented exercise. It is unclear whether these inherent or acquired components of aerobic fitness play a role in neurocognition. To isolate the potential role of inherent aerobic fitness, we exploited a rat model of high (HCR) and low (LCR) inherent aerobic capacity for running. At a baseline, HCR rats have two- to three-fold higher aerobic capacity than LCR rats. We found that HCR rats also had two- to three- fold more young neurons in the hippocampus than LCR rats as well as rats from the heterogeneous founder population. We then asked whether this enhanced neurogenesis translates to enhanced hippocampal cognition, as is typically seen in exercise-trained animals. Compared to LCR rats, HCR rats performed with high accuracy on tasks designed to test neurogenesis-dependent pattern separation ability by examining investigatory behavior between very similar objects or locations. To investigate whether an aerobic response to exercise is required for exercise-induced changes in neurogenesis and cognition, we utilized a rat model of high (HRT) and low (LRT) aerobic response to treadmill training. At a baseline, HRT and LRT rats have comparable aerobic capacity as measured by a standard treadmill fit test, yet after a standardized training regimen, HRT but not LRT rats robustly increase their aerobic capacity for running. We found that sedentary LRT and HRT rats had equivalent levels of hippocampal neurogenesis, but only HRT rats had an elevation in the number of young neurons in the hippocampus following training, which was positively correlated with accuracy on pattern separation tasks. Taken together, these data suggest that a significant elevation in aerobic capacity is necessary for exercise-induced hippocampal neurogenesis and hippocampal neurogenesis-dependent learning and memory. To investigate the potential for high aerobic capacity to be neuroprotective, doxorubicin chemotherapy was administered to LCR and HCR rats. While doxorubicin induces a progressive decrease in aerobic capacity as well as neurogenesis, HCR rats remain at higher levels on those measures compared to even saline-treated LCR rats. HCR and LCR rats that received exercise training throughout doxorubicin treatment demonstrated positive effects of exercise on aerobic capacity and neurogenesis, regardless of inherent aerobic capacity. Overall, these findings demonstrate that inherent and acquired components of aerobic fitness play a crucial role not only in the cardiorespiratory system but also the fitness of the brain.

A COMPARISON OF MARKET CLEARING TOOLS IN ELECTRICITY SYSTEMS

To maintain a strict balance between demand and supply in the US power systems, the Independent System Operators (ISOs) schedule power plants and determine electricity prices using a market clearing model. This model determines for each time period and power plant, the times of startup, shutdown, the amount of power production, and the provisioning of spinning and non-spinning power generation reserves, etc. Such a deterministic optimization model takes as input the characteristics of all the generating units such as their power generation installed capacity, ramp rates, minimum up and down time requirements, and marginal costs for production, as well as the forecast of intermittent energy such as wind and solar, along with the minimum reserve requirement of the whole system. This reserve requirement is determined based on the likelihood of outages on the supply side and on the levels of error forecasts in demand and intermittent generation. With increased installed capacity of intermittent renewable energy, determining the appropriate level of reserve requirements has become harder. Stochastic market clearing models have been proposed as an alternative to deterministic market clearing models. Rather than using a fixed reserve targets as an input, stochastic market clearing models take different scenarios of wind power into consideration and determine reserves schedule as output. Using a scaled version of the power generation system of PJM, a regional transmission organization (RTO) that coordinates the movement of wholesale electricity in all or parts of 13 states and the District of Columbia, and wind scenarios generated from BPA (Bonneville Power Administration) data, this paper explores a comparison of the performance between a stochastic and deterministic model in market clearing. The two models are compared in their ability to contribute to the affordability, reliability and sustainability of the electricity system, measured in terms of total operational costs, load shedding and air emissions. The process of building the models and running for tests indicate that a fair comparison is difficult to obtain due to the multi-dimensional performance metrics considered here, and the difficulty in setting up the parameters of the models in a way that does not advantage or disadvantage one modeling framework. Along these lines, this study explores the effect that model assumptions such as reserve requirements, value of lost load (VOLL) and wind spillage costs have on the comparison of the performance of stochastic vs deterministic market clearing models.