Plug-in hybrid electric vehicles (PHEVs) may become a substantial part of the transportation fleet on time scales of a decade or two. This dissertation investigates battery degradation, and how the introduction of PHEVs may influence the electricity grid, emissions, and petroleum use in the US. It examines the effects of combined driving and vehicle-to-grid (V2G) usage on the lifetime performance of relevant commercial Li-ion cells. The loss of battery capacity was quantified as a function of driving days as well as a function of integrated capacity and energy processed by the cells. The cells tested showed promising capacity fade performance: more than 95% of the original cell capacity remains after thousands of driving days worth of use. Statistical analyses indicate that rapid vehicle motive cycling degraded the cells more than slower, V2G galvanostatic cycling. These data are used to examine the potential economic implications of using vehicle batteries to store grid electricity generated at off-peak hours for off-vehicle use during peak hours. The maximum annual profit with perfect market information and no battery degradation cost ranged from ∼US$140 to $250 in the three cities. If the measured battery degradation is applied, however, the maximum annual profit decreases to ∼$10–120. The dissertation details the increase in electric grid load and emissions due to vehicle battery charging in PJM and NYISO with the current generation mix, the current mix with a $50/tonne CO2 price, and this case but with existing coal generators retrofitted with 80% CO2 capture. It also models emissions using natural gas or wind+gas. PHEV fleet percentages between 0.4 and 50% are examined. When compared to 2020 CAFE standards, net CO2 emissions in New York are reduced by switching from gasoline to electricity; coal-heavy PJM shows somewhat smaller benefits unless coal units are fitted with CCS or replaced with lower CO2 generation. NOX is reduced in both RTOs, but there is upward pressure on SO2 emissions or allowance prices under a cap. Finally the dissertation compares increasing the all-electric range (AER) of PHEVs to installing charging infrastructure. Fuel use was modeled using the National Household Travel Survey and Greenhouse Gasses, Regulated Emissions, and Energy Use in Transportation model. It was found that increasing AER of plug-in hybrids was a more cost effective solution to reducing gasoline consumption than installing charging infrastructure. Comparison of results to current subsidy structure shows various options to improve future PHEV or other vehicle subsidy programs.

Silicon Valley is a storied region regarded by many as a model for economic development. Many governments have attempted or considered implementing policies or projects aimed at re-creating the success of Silicon Valley. However, it is not clear that we truly know what led to Silicon Valley’s success, as existing work has not pursued industry-wide firm-level analyses to examine the mechanisms that allowed Silicon Valley to emerge as a key region. This work seeks to begin to address this literature gap in order to better inform regional economic development policy moving forward. In examining the development of Silicon Valley and the semiconductor industry, a detailed analysis of the technological developments leading to both transistors and integrated circuits was performed. From this analysis, it became clear that the nature and availability of knowledge changed significantly between the transistor and integrated circuit eras, with knowledge becoming more complex, tacit, and less available throughout the industry. From this understanding, specific predictions and hypotheses regarding firm and industry development were generated guided by existing theory. Using a novel dataset of US semiconductor production between 1947 and 1987, this dissertation examines empirically the development of the semiconductor industry to test these hypotheses. The results show that the mechanisms driving success differed between the two eras of the semiconductor industry. As the industry transitioned to the transistor era, existing electronics firms dominated the industry, which resulted in a build-up of transistor firms in the same clusters that previously produced electronics products; however, this was not the case as the industry transitioned to integrated circuits. The nature of the knowledge in the integrated circuit era allowed spinoff firms to emerge as an important force in the industry, out-performing incumbent firms, which ultimately led to the emergence of Silicon Valley as the primary semiconductor industry cluster. It is important to understand the technological context that created an opportunity for spinoff firms to fuel Silicon Valley’s ascension to significance within the industry, as this dissertation demonstrates that the applicability of existing theories regarding firm entry and development are influenced by the nature of technology. Understanding the conditions under which various mechanisms can be effective in promoting firm entry and performance, and thus regional clusters is vital in order to craft efficient public policy and projects aimed at building industry clusters in the future. This dissertation contributes greatly to that understanding.

A 2007 U.N. survey found that 54% of Americans advocate ―wait-and-see‖ behavior on policies that mitigate climate change, i.e., they infer that climate mitigation actions can be deferred until there are clear signs of danger. By evaluating different cognitive factors that influence human behavior, this thesis builds a framework that provides answers to an important question: why do people advocate wait-and-see behavior on climate change? One cognitive factor is misperceptions of feedback (i.e., ignorance of large feedback delays between CO2 emission decisions and the corresponding changes in CO2 concentration). Results reveal that the use of simulation tools, that provide repeated feedback about decision actions and corresponding consequences, is likely to enable people to overcome these misperceptions. A second factor is people‘s reliance on correlational or linear thinking (that the shape of CO2 emissions and CO2 concentration should look alike). Results reveal that the use of a physical representation (i.e., a picture of a problem in the form of a metaphor), simulation tools, and presenting problems such people‘s reliance on heuristics and biases enables them to make ecofriendly decisions is likely to enable people to overcome their correlational thinking. Other cognitive factors that affect people‘s wait-and-see behavior include people‘s risk and time preferences about future climate consequences when these consequences are either described or experienced. Results reveal that descriptive methods (e.g., books, newspapers, and reports) are likely to produce more wait-and-see behavior due to a high probability, small cost, and late timing of future consequences; whereas, experiential methods (e.g., movies, imagery, and games) are likely to produce more wait-and-see behavior due to a low probability, large cost, and early timing of future consequences. Policy implications suggest a careful design of descriptive and experiential climate risk communication methods, and the use of above described manipulations to improve people‘s decision making on climate change.

Renewable energy poses a challenge to electricity grid operators due to its variability and intermittency. In this thesis I quantify the cost of variability of different renewable energy technologies and then explore the use of reconfigurable distribution grids and pumped hydro electricity storage to integrate renewable energy into the electricity grid. Cost of Variability I calculate the cost of variability of solar thermal, solar photovoltaic, and wind by summing the costs of ancillary services and the energy required to compensate for variability and intermittency. I also calculate the cost of variability per unit of displaced CO2 emissions. The costs of variability are dependent on technology type. Variability cost for solar PV is $8-11/MWh, for solar thermal it is $5/MWh, and for wind it is around $4/MWh. Variability adds ~$15/tonne CO2 to the cost of abatement for solar thermal power, $25 for wind, and $33-$40 for PV. Distribution Grid Reconfiguration A reconfigurable network can change its topology by opening and closing switches on power lines. I show that reconfiguration allows a grid operator to reduce operational losses as well as accept more intermittent renewable generation than a static configuration can. Net present value analysis of automated switch technology shows that the return on investment is negative for this test network when considering loss reduction, but that the return is positive under certain conditions when reconfiguration is used to minimize curtailment of a renewable energy resource. Pumped Hydro Storage in Portugal Portugal is planning to build five new pumped hydro storage facilities to balance its growing wind capacity. I calculate the arbitrage potential of the storage capacity from the perspective of an independent storage owner, a thermal fleet owner, and a consumer-oriented storage owner. This research quantifies the effect storage ownership has on CO2 emissions, consumer electricity expenditure, and thermal generator profits. I find that in the Portuguese electricity market, an independent storage owner could not recoup its investment in storage using arbitrage only, but a thermal fleet owner or consumer-oriented owner may get a positive return on investment

Organizational learning and knowledge transfer are key elements within any firm when considering the firm’s competitive advantage and long-term goals. Yet, the roles of learning and knowledge transfer in a multi-product production setting are not well understood. Production and operations management literature suggests production of a variety of products is largely harmful, yet the organizational learning literature suggests there may be benefits to heterogeneity. This work explores the significance of a multi-product environment on organizational learning and knowledge transfer by studying a US-owned overseas manufacturing facility that is a leading producer of high technology hardware components. The firm produces 5 generations of high-volume focus products as well as a collection of non-focus products [an assortment of small volume products related to the focus products]. We draw on 10 years of firm archival data and qualitative data collected to shed insights into how different levels of product mix (5 generations of a focus product, thousands of minor variations on products to meet customer specifications, and an assortment of small volume products related to the focus product) impact organizational learning differently and why knowledge transfers across some products and not others by examining the role that processes play in these product transitions. Our results reconcile differences between the organizational learning and production and operations management literatures by finding support for both advantages and disadvantages to product mix on the production line depending on the extent of product differences. We find that short-term productivity improves with bounded diversity – specifically, when multiple generations of the same product are produced in the same facility. This positive impact on productivity of having multiple generations of the same product on the line may in part be explained by the firm’s ability to successfully transfer knowledge from older to newer generations of the product, improving long-term productivity, though we find benefits for focus product heterogeneity over and above the benefits from knowledge transfer. In contrast, we find short-term productivity is decreased when the production line is faced with variety across products that are too different from each other (e.g. different form factors) and across minor product variations (i.e. customer-specific product variations).

Electricity generated from wind power is both variable and uncertain. Wind forecasts provide valuable information for wind farm management, but they are not perfect. Chapter 2 presents a model of a wind farm with compressed air energy storage (CAES) participating freely in the day-ahead electricity market without the benefit of a renewable portfolio standard or production tax credit. CAES is used to reduce the risk of committing uncertain quantities of wind energy and to shift dispatch of wind generation to high price periods. Using wind forecast data and market prices from 2006 – 2009, we find that the annual income for the modeled wind-CAES system would not cover annualized capital costs. We also estimate market prices with a carbon price of $20 and $50 per tonne CO2 and find that the revenue would still not cover the capital costs. The implied cost per tonne of avoided CO2 to make a wind-CAES profitable from trading on the day-ahead market is roughly $100, with large variability due to electric power prices. Wind power forecast errors for aggregated wind farms are often modeled with Gaussian distributions. However, data from several studies have shown this to be inaccurate. Further, the distribution of wind power forecast errors largely depends on the wind power forecast value. The few papers that account for this dependence bin the wind forecast data and fit parametric distributions to the actual wind power in each bin. A method to model wind power forecast uncertainty as a single closed-form solution using a logit transformation of historical wind power forecast and actual wind power data is presented in Chapter 3. Once transformed, the data become close to jointly normally distributed. We show the process of calculating confidence intervals for wind power forecast errors using the jointly normally distributed logit transformed data. This method has the advantage of fitting the entire dataset with five parameters while also providing the ability to make calculations conditioned on the value of the wind power forecast. The model present in Chapter 3 is applied in Chapter 4 to calculate increases in net load uncertainty introduced from day-ahead wind power forecasts. Our analyses uses data from two different electric grids in the U.S. having similar levels of installed wind capacity with large differences in wind and load forecast accuracy due to geographic characteristics. A probabilistic method to calculate the dispatchable generation capacity required to balance day-ahead wind and load forecast errors for a given level of reliability is presented. Using empirical data we show that the capacity requirements for 95% day-ahead reliability range from 2100 MW to 5600 MW for ERCOT and 1900 MW to 4500 MW for MISO, depending on the amount of wind and load forecast for the next day. We briefly discuss the additional requirements for higher reliability levels and the effect of correlated wind and load forecast errors. Additionally, we show that each MW of additional wind power capacity in ERCOT must be matched by a 0.30 MW day-ahead dispatchable generation capacity to cover 95% of day-ahead uncertainty. Due to the lower wind forecast uncertainty in MISO, the value drops to 0.13 MW of dispatchable capacity for each MW of additional wind capacity. Direct load control (DLC) has received a lot of attention lately as an enabler of wind power. One major benefit of DLC is the added flexibility it brings to the grid. Utilities in some parts of the U.S. can bid the load reduction from DLC into energy markets. Forecasts of the resource available for DLC assist in determining load reduction quantities to offer. In Chapter 5, we present a censored regression model to forecast load from residential air conditioners using historical load data, hour of the day, and ambient temperature. We tested the forecast model with hourly data from 467 air conditioners located in three different utilities. We used two months of data to train the model and then ran day-ahead forecasts over a six week period. Mean square errors ranged from 4% to 8% of mean air conditioner load. This method produced accurate forecasts with much lower data requirements than physics based forecast models.

Demand response and dynamic pricing are touted as ways to empower consumers, save consumers money, and capitalize on the “smart grid” and expensive advanced meter infrastructure. In this work, I attempt to show that demand response and dynamic pricing are more nuanced. Dynamic pricing is very appealing in theory but the reality of it is less clear. Customers do not always respond to prices. Price differentials are not always large enough for customers to save money. Quantifying energy that was not used is difficult. In chapter 2, I go into more detail on the potential benefits of demand response. I include a literature review of residential dynamic pilots and tariffs to see if there is evidence that consumers respond to dynamic rates, and assess the conditions that lead to a response. Chapter 3 explores equity issues with dynamic pricing. Flat rates have an inherent cross-subsidy built in because more peaky customers (who use proportionally more power when marginal price is high) and less peaky customers pay the same rates, regardless of the cost they impose on the system. A switch to dynamic pricing would remove this cross subsidy and have a significant distributional impact. I analyze this distributional impact under different levels of elasticity and capacity savings. Chapter 4 is an econometric analysis of the Commonwealth Edison RTP tariff. I show that it is extremely difficult to find the small signal of consumer response to price in all of the noise of everyday residential electricity usage. Chapter 5 looks at methods for forecasting, measuring, and verifying demand response in direct load control of air-conditioners. Forecasting is important for system planning. Measurement and verification are necessary to ensure that payments are fair. I have developed a new, censored regression based model for forecasting the available direct load control resource. This forecast can be used for measurement and verification to determine AC load in the counterfactual where DLC is not applied. This method is more accurate than the typical moving averages used by most ISO’s, and is simple, easy, and cheap to implement.

The electricity sector is responsible for producing 35% of US greenhouse gas (GHG) emissions. Estimates suggest that ideally, the electricity sector would be responsible for approximately 85% of emissions abatement associated with climate polices such as America’s Clean Energy and Security Act (ACES). This is equivalent to ~50% cumulative emissions reductions below projected cumulative business-as-usual (BAU) emissions. Achieving these levels of emissions reductions will require dramatic changes in the US electricity generating infrastructure: almost all of the fossil-generation fleet will need to be replaced with low-carbon sources and society is likely to have to maintain a high build rate of new capacity for decades. Unfortunately, the inertia in the electricity sector means that there may be physical constraints to the rate at which new electricity generating capacity can be built. Because the build rate of new electricity generating capacity may be limited, the timing of regulation is critical—the longer the U.S. waits to start reducing GHG emissions, the faster the turnover in the electricity sector must occur in order to meet the same target. There is a real, and thus far unexplored, possibility that the U.S. could delay climate change policy implementation for long enough that it becomes infeasible to attain the necessary rate of turnover in the electricity sector. This dissertation investigates the relationship between climate policy timing and infrastructure turnover in the electricity sector. The goal of the dissertation is to answer the question: How long can we wait before constraints on infrastructure turnover in the electricity sector make achieving our climate goals impossible? Using the Infrastructure Flow Assessment Model, which was developed in this work, this dissertation shows that delaying climate change policy increases average retirements rates by 200-400%, increases average construction rates by 25-85% and increases maximum construction rates by 50-300%. It also shows that delaying climate policy has little effect on the age of retired plants or the stranded costs associated with premature retirement. In order for the electricity sector to reduce emissions to a level required by ACES while limiting construction rates to within achievable levels, it is necessary to start immediately. Delaying the process of decarbonization means that more abatement will be necessary from other sectors or geoengineering. By not starting emissions abatement early, therefore, the US forfeits its most accessible abatement potential and increases the challenge of climate change mitigation unnecessarily.

This thesis addresses the cost-effectiveness of curtailing a wind farm to regulate the electrical grid frequency and the hurricane risk to offshore wind farms in the eastern United States. Additionally, this thesis presents a new method to generate long periods of non-stationary wind speed time series data sampled at high rates by combining measured and simulated data. Paper 1 calculates the cost of curtailing the power output of a wind farm to provide a reserve of power to regulate the electrical grid frequency, as required by grid operators in several countries with high wind-power penetrations. The simulations in Paper 1 show that it is most efficient to curtail a few turbines deeply rather than curtail all turbines in a wind farm equally. Compared to regulation prices in the Texas (ERCOT) market in 2007-2009, a curtailed wind farm would be cost-competitive with conventional generators less than 1% of the time. Paper 2 supports the simulations in Paper 1 by developing a method to combine long periods of low-frequency wind speed data with realistic simulated high-frequency turbulence. The combined time series of wind speeds retains the non-stationary characteristics of wind speed, such as diurnal variations, the passing of weather fronts, and seasonal variations, but gives a much higher sampling rate. Papers 3 and 4 estimate the hurricane risks to current designs of offshore wind turbines in the U.S. Paper 3 develops analytical probability distributions based on historical hurricane records to predict the distribution of damages to a single wind farm in a given location. Paper 4 uses simulated hurricanes with realistic statistical properties to estimate the correlated risks to all the wind farms in a region and estimate the distribution of aggregate losses over different periods. Both papers find hurricane risks are small for current turbine designs in New England and the Mid-Atlantic, but the risks in the Gulf of Mexico and the Southeast are significant enough to warrant new, stronger designs. Hurricane risks could be reduced almost an order of magnitude by ensuring that turbines can continue yawing to track the wind direction even if grid power is lost.

Content-centric networking (CCN) has emerged as a dominant paradigm for future Internet architecture design due to its efficient support for content dissemination, which currently dominates Internet use. This dissertation shows how economic and social welfare analysis can be used to inform the design of a CCN architecture that provides network stakeholders with incentives to deploy and use. Firstly, the dissertation investigates the economic incentives of different stakeholders to deploy content-centric network Internet architectures and shows that network operators will fail to deploy sufficient storage infrastructure to support CCN without payment ows from publishers. However, the level of payment required differs for different network players, which gives them different competitive advantages in providing storage infrastructure and content delivery services. Secondly, it evaluates the social welfare implications of different storage deployment scenarios in a CCN-based architecture and identifies two deployments that maximize social welfare. In the first, edge networks provide the storage infrastructure through a transaction broker. In the second, edge networks pay third-parties an amount, equivalent to the realized benefits from a storage node, to deploy storage infrastructure in the network. All other deployment scenarios lead to a deadweight loss. Thirdly, the dissertation identifies content delivery functionalities that break in a CCN-based architecture and shows how these functionalities can be successfully replicated and enhanced by a careful design of the structure of routable content, content naming and the meta-information added to content. The proposed design supports several content delivery applications and can be easily extended to other networking principals. Finally, the dissertation identifies and discusses threats in the CCN content delivery model and proposes some mechanisms to address these threats. In addition, the dissertation identifies some policy implications of the CCN content delivery model and proposes some policy interventions that may lead to desirable deployment outcomes.