This dissertation demonstrates how flexibility in hourly electricity operations can impact long-term planning and analysis for future power systems, particularly those with substantial variable renewables (e.g., wind) or strict carbon policies. Operational flexibility describes a power system’s ability to respond to predictable and unexpected changes in generation or demand. Planning and policy models have traditionally not directly captured the technical operating constraints that determine operational flexibility. However, as demonstrated in this dissertation, this capability becomes increasingly important with the greater flexibility required by significant renewables (>=20%) and the decreased flexibility inherent in some low-carbon generation technologies. Incorporating flexibility can significantly change optimal generation and energy mixes, lower system costs, improve policy impact estimates, and enable system designs capable of meeting strict regulatory targets.

Methodologically, this work presents a new clustered formulation that tractably combines a range of normally distinct power system models, from hourly unit-commitment operations to long-term generation planning. This formulation groups similar generators into clusters to reduce problem size, while still retaining the individual unit constraints required to accurately capture operating reserves and other flexibility drivers. In comparisons against traditional unit commitment 3 formulations, errors were generally less than 1% while run times decreased by several orders of magnitude (e.g., 5000x). Extensive numeric simulations, using a realistic Texas-based power system show that ignoring flexibility can underestimate carbon emissions by 50% or result in significant load and wind shedding to meet environmental regulations.

Contributions of this dissertation include: (1) Demonstrating that operational flexibility can have an important impact on power system planning, and describing when and how these impacts occur; (2) Demonstrating that a failure to account for operational flexibility can result in undesirable outcomes for both utility planners and policy analysts; and (3) Extending the state of the art for electric power system models by introducing a tractable method for incorporating unit commitment based operational flexibility at full 8760 hourly resolution directly into planning optimization. Together these results encourage and offer a new flexibility-aware approach for capacity planning and accompanying policy design that can enable cleaner, less expensive electric power systems for the future.

[Thesis submitted October 2012; Doctoral Degree date February 2013]

The growth of location-constrained renewable generators and the integration of electricity markets in the United States and Europe are forcing transmission planners to consider the design of interconnection-wide systems. In this context, planners are analyzing major topological changes to the electric transmission system rather than more traditional questions of system reinforcement. Unlike a regional reinforcement problem where a planner may study tens of investments, the wide-area planning problem may consider thousands of investments. Complicating this already challenging problem is uncertainty with respect to future renewable-generation location. Transmission access, however, is imperative for these resources, which are often located distant from electrical demand. This dissertation frames the strategic planning problem and develops dimensionality reduction methods to solve this otherwise computationally intractable problem.

This work demonstrates three complementary methods to tractably solve multi-stage stochastic transmission network expansion planning. The first method, the St. Clair Screening Model, limits the number of investments which must be. The model iteratively uses a linear relaxation of the multi-period deterministic transmission expansion planning model to identify transmission corridors and specific investments of interest. The second approach is to develop a reduced-order model of the problem. Creating a reduced order transformation of the problem is difficult due to the binary investment variables, categorical data, and networked nature of the problem. The approach presented here explores two alternative techniques from image recognition, the Method of Moments and Principal Component Analysis, to reduce the dimensionality. Interpolation is then performed in the lower dimensional space. Finally, the third method embeds the reduced order representation within an Approximate Dynamic Programming framework. Approximate Dynamic Programming is a heuristic methodology which combines Monte Carlo methods with a reduced order model of the value function to solve high dimensionality optimization problems. All three approaches are demonstrated on an illustrative interconnection-wide case study problem considering the Western Electric Coordinating Council.

The US Federal Aviation Administration (FAA) has a goal that one billion gallons of renewable jet fuel is consumed by the US aviation industry each year from 2018. We examine the cost to US airlines of meeting this goal using renewable fuel produced from a Hydroprocessed Esters and Fatty Acids (HEFA) process from renewable oils. Our approach employs an economy-wide model of economic activity and energy systems and a detailed partial equilibrium model of the aviation industry. If soybean oil is used as a feedstock, we find that meeting the aviation biofuel goal in 2020 will require an implicit subsidy to biofuel producers of $2.69 per gallon of renewable jet fuel. If the aviation goal can be met by fuel from oilseed rotation crops grown on otherwise fallow land, the implicit subsidy is $0.35 per gallon of renewable jet fuel. As commercial aviation biofuel consumption represents less than two per cent of total fuel used by this industry, the goal has a small impact on the average price of jet fuel and carbon dioxide emissions. We also find that, as the product slate for HEFA processes includes diesel and jet fuel, there are important interactions between the goal for renewable jet fuel and mandates for ground transportation fuels.

Natural gas vehicles have the prospects of making substantial contributions to transportation needs. The adoption of natural gas vehicles could lead to impacts on energy and environmental systems. An analysis of the main factors and trends that affect adoption of natural gas vehicles such as vehicle costs, infrastructure costs, and fuel economics was performed. The fuel cost analysis showed that assuming production and distribution at scale, liquefied natural gas (LNG) can be competitive as a diesel fuel substitute for heavy duty vehicles in the US, and also in EU and China. A methodology of incorporating heavy duty natural gas vehicles into a computable general equilibrium (CGE) economic modelling was developed to investigate the potential adoption and impacts. Modelling variables such as vehicle and infrastructure costs were tested and several scenarios were applied to examine the general equilibrium impacts on natural gas vehicle adoption and the general equilibrium impacts of resulting natural gas vehicle adoption. Climate policy scenarios were also developed and tested. In the base case scenario, results showed significant adoption of LNG trucks (Class 8) in the US, with 10% penetration of heavy duty trucks by 2020 and up to 100% by 2040. In China and the EU, adoption was projected to be slower due to higher natural gas prices. In the US, introduction of LNG trucks resulted in moderately higher natural gas prices, slightly lower oil prices, and a small reduction in total GHG emissions, relative to scenarios without LNG truck availability. The development of natural gas fuelled transportation is still in its infancy and CGE modelling offers a tool that can be applied to test a wide range of assumptions of cost development and relative prices.