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- Title
- POWER GRID OPERATION RISK MANAGEMENT: V2G DEPLOYMENT FOR SUSTAINABLE DEVELOPMENT
- Creator
- Haddadian, Ghazale J.
- Date
- 2014, 2014-05
- Description
-
The production, transmission, and delivery of cost–efficient energy to supply ever-increasing peak loads/demands along with a quest for...
Show moreThe production, transmission, and delivery of cost–efficient energy to supply ever-increasing peak loads/demands along with a quest for developing a low-carbon economy require significant evolutions in the power grid operations. Lower prices of vast natural gas resources in the United States, Fukushima nuclear disaster, higher and more intense energy consumptions in China and India, issues related to energy security, and recent Middle East conflicts, have urged decisions makers throughout the world to look into other means of generating electricity locally. As the world look to combat climate changes, a shift from carbon-based fuels to non-carbon based fuels is inevitable. It is possible to knock a lot of carbon out of the electric power system through large-scale integrations of renewable sources. However, the variability of distributed generation assets (such as wind and solar) in the electricity grid has introduced major reliability challenges/risks for power grid operators. While spearheading sustainable and reliable power grid operations, this dissertation develops a multi-stakeholder approach to power grid operation design; aiming to address economic, security, and environmental challenges of the constrained electricity generation. It investigates the role of Electric Vehicle (EV) fleets integration, as distributed and mobile storage assets to support high penetrations of variable and renewable energy sources, in the power grid. The vehicle-to-grid (V2G) concept is considered to demonstrate the bidirectional role of EV fleets both as a provider and consumer of energy in securing a sustainable power grid operation. The V2G concept is regarded as a novel, low-cost, low-emission and sustainable strategy that can address xv challenges involve with using renewable energy sources, which require means of storing large quantities of energy. The proposed optimization modeling is the application of Mixed-Integer Linear Programing (MILP) to large-scale systems to solve the hourly security-constrained unit commitment (SCUC) – an optimal scheduling concept in the economic operation of electric power systems. The Monte Carlo scenario-based approach is utilized to evaluate different scenarios concerning the uncertainties in the operation of power grid system. Further, in order to expedite the real-time solution of the proposed approach for large-scale power systems, this dissertation considers a two-stage model using the Benders Decomposition (BD) and applies the BD method to the hourly SCUC solution of electric power systems with significant uncertainties. The numerical simulation demonstrate that the utilization of smart EV fleets in power grid systems would ensure a sustainable grid operation with lower carbon footprints, smoother integration of renewable sources, higher security, and lower power grid operation costs. Further, simulation results indicate that intelligent-controlled mode, in which electric power system operators control the EV fleets charge/discharge decisions based on the system operation requirements, is more effective compare to the rule-based mode, in which consumers control charging/discharging decisions. The numerical simulations, additionally, illustrate the effectiveness of the proposed MILP approach and its potentials as an optimization tool for sustainable operation of large scale electric power systems.
PH.D in Management Science, May 2014
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- Title
- ESSAYS ON DISTRIBUTIONALLY ROBUST PORTFOLIO OPTIMIZATION
- Creator
- Ousawat, Thitapon
- Date
- 2013, 2013-07
- Description
-
Interest in distributionally robust optimization has been increasing recently. In this dissertation, we review recent developments in the...
Show moreInterest in distributionally robust optimization has been increasing recently. In this dissertation, we review recent developments in the literature in this eld and propose a model for distributionally robust mean-risk portfolio optimization. The model optimizes a risk-averse objective function with the worst-case return as reward and worse-case conditional Value-at-Risk as the risk measure. The model considers ambiguity in the distribution of data used to estimate the asset returns in the optimization model by creating an ambiguity set using -divergence measures which measure the distance between vectors. A numerical example is shown using the Kullback-Leibler divergence measure as the -divergence measure. A model for distributionally robust portfolio optimization with transaction costs is used to compare the performance of a distributionally robust mean-CVaR portfolio with the nominal as well as equally-weighted portfolio. The result shows that, under certain conditions, the distributionally robust model performs better than both the nominal and equally-weighted portfolio.
PH.D in Management Science, July 2013
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- Title
- International Bond Portfolio: Evidence from Emerging Markets
- Creator
- Wang, Jinghua
- Date
- 2012-08-20, 2012-07
- Description
-
Allocating capital to fixed income instruments issued by emerging markets (EMs), governments may provide significant benefits to both the...
Show moreAllocating capital to fixed income instruments issued by emerging markets (EMs), governments may provide significant benefits to both the investors and issuers of these instruments. For investors, emerging market instruments may offer a significant risk premium relative to conventional investments in developed markets (DMs) bonds. Furthermore, EMs bonds offer potential diversification benefits because these bonds are not strongly correlated with DMs instruments. For emerging market government issuers, access to global fixed income markets is likely to improve liquidity and offer lower borrowing costs relative to a strategy focused purely on the domestic market. Access to global capital provides these governments with the opportunity to invest in infrastructure projects that promote economic growth and development. Over the past fifty years, economic growth in emerging markets has been supported by investments in capital and technology from the developed world. The benefit of this development for the emerging markets, as measured by growth in income, employment, and wealth, is immediately apparent. There have also been significant advantages for the developed world through opportunities for higher risk adjusted returns from investments in emerging markets. For the most part, the benefits of diversification into emerging markets have focused on equity markets. In this dissertation, the focus is on investments in fixed income instruments. Specifically, the dissertation explores the performance benefits of DMs combined with EMs. It first identifies the potential diversification and describes the financial integration for incorporating EMs bonds into DMs government bond portfolios. In the second phase, it constructs the dynamic linear regression models and conducts the mean-variance tests to demonstrate the incremental benefit of the strategy. In the last phase, a robust test examines the strength of bond portfolio performance between DMs with EMs and the U.S. 7-10 year government bond index. The empirical analysis in this dissertation focuses on three DMs sources of bonds and four EMs regions. Since the EMs are evolving rapidly, and since the global financial markets have also been subject to erratic fluctuations during the global financial crisis, the empirical models employed in the dissertation do not rely on stationarity assumptions. Instead, Kalman Filter (KF) procedures are employed that generate the time-varying coefficients in the multi-factor models in response to new conditions in the markets. The outputs from the KF are used as inputs in the factor model, and the outputs from the factor models are used as inputs in a Markowitz style mean-variance optimization model. This study explores the benefits of the diversification of global government bond portfolio, and provides complete performance evaluations of DMs with or without EMs. The study examines: i) the benefits of inclusion of EMs bonds in DMs; ii) the degrees of financial integration among the research markets; iii) the correlation of the macro-economic factors in the multi-factor models; iv) the relative bond returns of dynamic factor models with time-varying coefficients; and v) the robust tests of bond portfolio performance between DMs with EMs and bond index. The results of this study provide important implications for global investors by identifying diversification gains in EMs.
Ph.D. in Management Science in Finance, July 2012
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