published_or_final_version / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

Wind power generation has experienced an explosive growth worldwide. It is a promising renewable energy source to countries that are short of fossil fuels, e.g. China. While wind power is a distinctive direction to go for, it is still necessary to examine the rationale behind such investing mania, and this thesis analyzes the issue by collectively investment modeling. For investment analysis, it is necessary to first identify the relevant market background before inferring to any analytical model. Chapter 2 identifies a number of wind power investment scenarios in accordance to modern electricity market regime, primarily American and European structures. Among them, two main scenarios are investigated and modeled subsequently: fixed tariff wind power project invested by independent power producer and wind power project undertaken by utility. It has to be emphasized that different market scenarios would lead to different modeling methodologies for best representing the reality. Wind power is intermittent and uncertain. One way to describe the probabilistic energy production is by statistical characterization of wind power in a period of time. Chapter 3 presents a standalone analytical model of the wind power probability distribution and its higher order statistics. Large-scale deployment of wind power would influence power system in unprecedented ways. High penetration wind power poses a need of refinement to existing methodologies on production costing and reliability evaluation. The applications of the probabilistic wind power model to these topics are outlined in this chapter. In Chapter 4, investment of fixed tariff wind power project is analyzed. Operation of wind farm is very passive and as long as wind keeps blowing, such wind power investment has minimal risk in annual revenue. The low-risk profile facilitates debt financing. This leads to the attempt to manipulate the project capital structure to maximize the project levered value. Yet the default probability is raised and associated with a subjective value of default probability there is a value-at-risk debt level. I therefore propose an optimization formulation to maximize the wind power project valuation with debt as decision variable subject to the value-at-risk debt constraint. Apart from independent wind power producers, many policy and market factors driving wind power development are actually put on the utility side, e.g. Renewable Portfolio Standard (Renewable Energy Target) in U.S. (Europe) and Green Power Programs. It implies that utility has to have wind power (or other renewable) capacity ready by a certain date. In practice, utility may take action earlier if conditions are favorable or optimal. The conditions considered here are fossil fuel prices or in more general setting, electricity contract prices. Define the total fuel cost saving from conventional units as the benefit of wind power. If fuel prices are high enough, substituting load demand by wind energy is profitable, vice versa. The investment decision is analogous to premature exercising of an American option, in which the wind power project is modeled as real option. Chapter 5 offers detailed formulation of this idea. / published_or_final_version / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

published_or_final_version / Architecture / Master / Master of Landscape Architecture

published_or_final_version / Electrical and Electronic Engineering / Master / Master of Philosophy

published_or_final_version / Electrical and Electronic Engineering / Master / Master of Philosophy

published_or_final_version / Electrical Engineering / Doctoral / Doctor of Philosophy

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No description available.

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This thesis is the result of an investigation to assess the potential of the continuation method to solve the minimum cost optimal power flow problem. For this purpose, a restart homotopy continuation method algorithm is developed, which contains in essence two phases. / The pertinent steps of the first phase are as follows: (1) Create a sub-problem of the complete optimal power flow problem by relaxing boundary limits on all functional variables, namely voltages at load buses, line flows and reactive generations. (2) Parameterize a subset of the whole set of controls which comprises initially of tap-changers, phase-shifters, shunt controllers, and the voltages at generation buses. (3) Optimize the resulting problem. / The solution in step (3) is used as an initial starting point in a continuation process, designed to track this solution to the optimal solution of the sub-problem defined in step (1). The tracking is accomplished via a predictor-corrector path following algorithm embodying certain special features, such that the solution accuracy can be improved to any desired degree through a flexible restart feature developed in this study. Within the tracking process only a subset (identified in step 2 above) of the whole set of controls require specific monitoring for break-points. This feature greatly reduces the computational burden. Termination of the first phase marks an operating point in which all controls are strictly feasible. / If, following the termination of the first phase, functional variables previously ignored prove to be within their respective bounds, the solution to the sub-problem becomes the solution to the complete optimal power flow problem. However, should functional variables violate their bounds the second phase of the algorithm is invoked, which in essence creates a new sub-problem by changing the roles of the control and violated dependent variables, such that the newly modified sub-problem maintains the same basic structure as its predecessor. / Phase I is invoked again at this juncture to solve the modified sub-problem. This process is repeated in cycles until the Kuhn-Tucker optimality conditions are satisfied. Simulations suggest that convergence is usually achieved within two or three Phase I/II cycles. / This being a method unique to the minimum cost optimal power flow problem, numerous examples (up to 118 buses) have been tested and compared against the commercial code MINOS. The newly proposed algorithm appears to be faster and more reliable.