Browsing by Subject "Renewable Energy"
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Item NOx reduction with the use of feedlot biomass as a reburn fuel(2009-05-15) Goughnour, Paul GordonCoal fired power plants produce NOx at unacceptable levels. In order to control these emissions without major modifications to the burners, additional fuel called reburn fuel is fired under rich conditions (10-30 % by heat) after the coal burners. Additional air called overfire air (about 20 % of total air) is injected in order to complete combustion. Typically reburn fuel is natural gas (NG). From previous research at TAMU, it was found that firing feedlot biomass (FB) as reburn fuel lowers the NOx emission at significant levels compared to NG. The present research was conducted to determine the optimum operating conditions for the reduction of NOx. Experiments were performed in a small scale 29.3 kW (100,000 BTU/hr) reactor using low ash partially composted FB (LA PC FB) with equivalence ratio ranging from 1 to 1.15. The results of these experiments show that NOx levels can be reduced by as much as 90% - 95 % when firing pure LA PC FB and results are almost independent of. The reburn fuel was injected with normal air and then vitiated air (12.5 % O2); further the angles of reburn injector were set normal to the main gas flow and at 45-degrees upward. For LA PC FB no significant changes were observed; but high ash PC FB revealed better reductions with 45-degrees injector and vitiated air. This new technology has the potential to reduce NOx emissions in coal fired boilers located near cattle feedlots and also relieves the cattle industry of the waste.Item Predictive and Corrective Scheduling in Electric Energy Systems with Variable Resources(2014-11-05) Gu, YingzhongIn the past decade, there has been sustained efforts around the globe in developing renewable energy-based generation in power systems. However, many renewables such as wind and solar are variable resources. They pose significant challenges to near real-time power system operations. This dissertation focuses on introducing and testing advanced scheduling algorithms for electric power systems with high penetration of variable resources. A novel predictive and optimal corrective look-ahead dispatch framework for real-time economic operation is proposed. This dissertation has four key parts. First, the basic framework of look-ahead dispatch is introduced. Different from conventional static economic dispatch, look-ahead dispatch is the fundamental function for future power system scheduling. Taking the whole dispatch horizon into account, look-ahead dispatch has a better economic performance in scheduling the resources in power systems. The decision-making of look-ahead dispatch is cost-effective, especially when handling with high penetration of variable resources. Second, we study the benefits of look-ahead dispatch in system security enhancement. An early detection algorithm is proposed to predict and identify potential security risks in the system. The proposed optimal corrective measures can be computed to prevent system insecurity at a minimized cost. Early awareness of such information is of vital importance to the system operators for taking timely actions with more flexible and cost-effective measures. Third, novel statistical wind power forecast models are presented, as an effort to reduce the uncertainty of renewable forecast to support the look-ahead economic dispatch and security management. The forecast models can produce more accurate forecast results by leveraging the spatio-temporal correlation in wind speed and direction data among geographically dispersed wind resources. Fourth, we propose a stochastic look-ahead dispatch (LAED-S) module to handle the high uncertainty in renewable resources. Even with state-of-the-art forecast technology, the near-real-time operational uncertainty from renewable resources cannot be eliminated. Given the uncertainty level, a conventional deterministic approach is not always the best option. The proposed LAED-S is able to judge whether a stochastic approach is preferred. The innovative computation algorithm of LAED-S leverages the progressive hedging and L-shaped method to produce good stochastic decision-making in a more efficient manner. Numerical experiments of a modified IEEE RTS system and a practical system are conducted to justify the proposed approaches in this dissertation. This framework can directly benefit the power system operation in moving from a static, passive real-time operation into a predictive and corrective paradigm.