Search

The electricity sector contributes to a quarter of global greenhouse emissions, and managing its evolution is a critical sustainability challenge. The context for the development and operation of electricity grids has dramatically changed in recent years. Wind and solar power have become much less expensive. Lower costs combined with increased policy action to address carbon emissions is leading to substantial shares of electricity generated by intermittent renewables. Maintaining a stable electricity supply with intermittency is a critical challenge; storage and natural gas are possible solutions. While policymakers promote storage as green grid technology, low-cost natural gas from hydrofracturing extraction raises the economic hurdle for storage. Researchers have developed complicated energy system models to help plan grids in the face of the above trends. The research in this dissertation introduces new modeling features that affect the economic and environmental outcomes of the adoption of renewable and storage technologies. First, prior models that explore the future build-out of electricity grids are nearly always deterministic, i.e., they assume that decision-makers have perfect information. Here a stochastic optimization grid expansion model is developed that presumes that expected future fluctuations, e.g. in fuel prices, influence build-out decisions. This stochastic model thus includes uncertainty and risk as core elements: Grid build-out depends on the distribution of system costs. A genetic algorithm with Monte-Carlo simulation is used for co-optimization using two objective functions: “risk-neutral,” which optimizes to minimize average system cost and “risk-averse,” which optimizes to minimize average of the top 5% of costs (also called 95% Conditional Value at Risk (CVaR)). This model is tested for the US Midwest regional grid. The results show that the risk-averse scenario does not increase mean system costs but adds significantly more wind. These results corroborate prior work showing that electricity system costs can be surprisingly inelastic to renewable adoption and further introduces quantification of how increased renewables lowers cost risk. Second, the economic and environmental performance of storage is complicated by how its introduction affects the operation of both renewable and fossil plants. In this dissertation, a model is developed that accounts for how storage operation would affect prices on the grid and in turn, the operational schedule that yields optimal revenue. Results from modeling the US Midwest region shows that this treatment of storage as a “price maker” affects results. The model indicates that storage increases carbon emissions when it enables a high emissions generator, such as a coal plant, to substitute for a cleaner plant, such as natural gas. In this case, low cost; efficient natural gas generation is relatively better than coal to realize emissions reductions with storage under economic arbitrage until renewables dominate the grid mix. Third, the operational strategies of energy storage alter the generation and profits of the other electricity generation systems. The operational effects of storage on the change in generation is investigated for all the eGRID subregions across the US based on actual historical electricity prices and the generation mix for the year 2016. Results show that storage increases the coal generation and affects the natural gas generation in the west – except in California and the Midwest regions of the US; and increases the generation of the natural gas in the eastern US regions. California, upstate New York and New England regions show an exception with an increase in natural gas generation and decrease in coal generation. The model also investigates the operational effects of storage on the profits of other generating units in California, Midwest and New York regions. Profits of other generating units are significantly affected when large capacities of storage operate as price-makers. Coal has a small increase in profits by 2% and all the other fuels continue to see a decline in profits in New York and the Midwest regions. The decrease in profits of the other generating units is because of the offset/retirements of the peaker natural gas plants that set the electricity prices. On the other hand, in California, the profits for renewables increase from the increase in electricity clearing prices set by the natural gas combined cycle plants to meet the additional demand from the storage charging.

The objective of this thesis is to simulate the construction of thermal energy storage systems for nuclear power plants in the ERCOT grid. Steam accumulators were selected as the thermal energy storage technology. A thermo-economic model was used to estimate the operating and cost parameters for sixteen different steam accumulator designs. A new capacity expansion model of the ERCOT grid was built on top of an existing production cost model for wholesale electricity market simulations. Sixteen permutations of four scenario pairs were simulated to illustrate the uncertainty of future market conditions. It was optimal to build steam accumulators in three of the permutations. Scenarios common to these per-mutations were high future natural gas prices (three permutations), aggressive capital cost declines for solar PV and wind generators (three), high load growth (two), and a carbon tax (two). This suggests that large-scale thermal energy storage systems may be most successful in future markets under these conditions.

Shippers and recipients expect transportation companies to provide more than just the movement of a package between points; certain information must be available to them as well, to enable forecasts and plans within the supply chain. The transportation companies also need the information flow that undergirds a transportation grid, to support ad-hoc routing and strategic structural re-alignment of business processes. This research delineates the information needs for an expanding air cargo network, then develops a new model of the information technologies needed to support expansion into a new country. The captured information will be used by shippers, recipients, and the transportation provider to better guide business decisions. This model will provide a method for transportation companies to balance the tradeoffs between the operating efficiencies, capital expenditures, and customer expectations of their IT systems. The output of the model is a list of technologies – optimized by cost – which meet the specific needs of internal and external customers when expanding air cargo networks into a new country.

Abstract: One of the most stunning results of information theory is the channel coding theorem addressing the maximum rate of reliable communication over a noisy channel, known as channel capacity. In this thesis, we consider two problems emerging from the classic channel coding theorem. First, we study the extremal problems of the channel reliability function, which is the exponent with which the probability of making a wrong decision vanishes. To this end, we introduce a set of fundamental channels which exhibit significant monotonicity properties and invoke the theory of Chebychev systems to utilize such properties. We show that the binary symmetric channel (BSC) and binary erasure channel (BEC), which happen to be among the fundamental channels, are the two extremes of the channel reliability function. Also, we show that given a rate and a probability of error as a performance measure, BSC (BEC) needs the longest (shortest) code length to achieve such performance. While the first problem is pure theoretical, the second problem addresses a challenging practical scenario. The most fundamental assumption in the classic channel coding theorem is that we receive as many symbols as we send. In reality, however, this is not always true, e.g., a miss-sampling at a conventional receiver might duplicate a symbol. The extra symbol confuses a receiver as it has no clue about the position of duplication. Such scenarios are collectively known as channels with synchronization errors. Unlike their classic counterparts, there is only little known about either the capacity or coding techniques for channels with synchronization errors, even in their simplest forms. In this part, we study the duplication channel by introducing a series expansion for its capacity.

As more renewable resources are added into the grid and environmental regulations are imposed to reduce emissions, there will be dramatic changes in the generation portfolio. Assessing the impact of these changes is important for policy makers, market participants, and general public to understand trends in the electricity market. This paper addresses this issue by analyzing how the ERCOT market is affected by CO2 penalty and wind penetration. In order to assess the future power system, the study model should represent the long term dynamics of various factors to find out how investment decisions are made economically in a competitive market with appropriate assumptions. Another important aspect is the short term market dynamics from real operation of power system. For this study, AURORAxmp, a commercially available market simulator, is utilized to capture both long term and short term dynamics. This study runs 5 different scenarios: two base cases with and without CO2 price, 20%, 27%, and 33% wind penetration level. The result shows that, increasing wind penetration reduces production and capacity of both coal and gas units, electricity market prices, and amount of emissions. However, increasing wind penetration has greater impacts on a decrease in generation from thermal units than reduction in thermal capacity, resulting in 11.4% capacity value of wind power. The study also confirms that CO2 price impacts capacity and generation of coal (negatively) and gas (positively) units in opposite ways, and reduces emission, but increases power prices and generation cost. Especially, the impact on retirement of coal units is noticeable. Almost half of the current coal capacity (19 GW), 9,390 MW, is retired by 2040 in this study.

Corn-based ethanol is the leader of sustainable sources of energy in the United States due to the abundance of corn and the popularity of ethanol-gasoline mixes. Over the past decade, ethanol production has risen from 1.5 million gallons in 1999 to 13 million gallons in 2011. This increase in production requires expansion of ethanol plants. Since Nebraska is the second highest producer of ethanol, we focus our research on the expansion of ethanol plants in Nebraska. The aim of this study is to develop an optimization model for capital investments in ethanol in Nebraska and a medium sized ethanol plant with 100 million gallons capacity in 2011. The model is developed for a firm in Nebraska and uses a planning horizon of five years. The problem is formulated as a dynamic programming model and solved using spread sheets. The data used are gathered from published papers, USDA reports, official Nebraska government website and Renewable Fuel Association reports (RFA). We find that the best strategy for a medium sized plant with capacity of 100 million gallons is to expand the capacity by 50 million gallons in the first year and reject the decision to expand in the following years up to 2016. The best expansion for ethanol in Nebraska is 200, 100 and 100 million gallon for 2012, 2013 and 2014 respectively and no expansion in 2015 and 2016. A scenario analysis is used to illuminate the decision space for different scenarios of profit margin and ethanol demand fluctuations. Adviser: Fred Choobineh

This thesis is motivated by the investment problems pharmaceutical manufacturing firms face when introducing new drug products.We consider two different types of resources that play a role in determining the initial production capacity in pharmaceutical manufacturing:generic production resources, and specialized facilities and equipment.Due to the differences in availability of {each resources} type and the dynamics of information the firm receives with respect to the underlying uncertainties, the investment problems posed by these two resource types are very different. We build two separate dynamic optimization models to analyze the respective investment strategies.For procurement of generic resources, we consider the firm facing random demand while the drug approval arrives at a random time. The firm can either increase or decrease inventory of the resources by buying or selling on a spot market where price fluctuates randomly over time.The firm's goal on this operation is to maximize expected discounted profit over the procurement process.We first show that this optimization problem is equivalent to a two-dimensional singular control problem. We then show that the optimal policy is completely characterized by a simple price-dependent two-threshold policy. For specialized equipment, we consider a model where the firm must balance two conflicting objectives: on one hand, the delay in scaling-up production once the product is approved must be minimized, and on the other hand, the risk of investing in ultimately unused capacity must be minimized. We develop a stylized model of this type of capacity investment problem, where the firm re-evaluates its capacity investment strategy as information about the potential success of the product is continually updated (for example, via clinical trial results).We identify settings in which by continually reviewing the building strategy, the firm can substantially reduce both the delay of the commercial launch of the new product, and the risk of lost investment.Although, our focus here is on the investment decisions in introducing a new drug product in the pharmaceutical industry, the models described in the following subsections can be applied to the introduction of products which require specialized equipment to manufacture and have a long research and development phase.