High expansion of power demand is expected in the Upper Rio Grande region (El Paso, Hudspeth, Culberson, Jeff Davis, Presidio and Brewster counties) as a result of both electrical demand growth and decommissioning of installed capacity.
On the supply side a notable deployment of renewable power technologies can be projected owing to the recent introduction of a new energy policy in Texas, which attempts to reach 10,000 installed-MWe of renewable capacity for 2025. Power generation fueled by natural-gas might consistently expand due to the encouraged use of this fuel. In this context the array of participating technologies can be optimized, which, within a sustainability framework, translates into a multidimensional problem. The solution to the problem is presented through this dissertation in two main parts.
The first part solves the thermodynamic-environmental problem through developing a dynamic model to project maximum allowable expansion of technologies. Predetermined alternatives include diverse renewable energy technologies (wind turbine, photovoltaic conversion, hybrid solar thermal parabolic trough, and solid oxide fuel cells), a conventional fossil-fuel technology (natural gas combined-cycle), and a breakthrough fossil-fuel technology (solid oxide fuel cells).
The analysis is based on the concept of cumulative exergy consumption, expanded to include abatement of emissions. A Gompertz sigmoid growth is assumed and constrained by both exergetic self-sustenance and regional energy resource availability. This part of the analysis assumes that power demand expansion is met by full deployment of alternative technologies backed up by conventional technology. Results show that through a proper allowance for exergy reinvestment the power demand expansion may be met largely by alternative technologies minimizing the primary resource depletion.
The second part of the study makes use of the dynamic model to support a multi-objective optimization routine, where the exergetic and economic costs are established as primary competing factors. An optimization algorithm is implemented using the constraint method.
The solution is given as Pareto optimality with arrays for minimum cost and possible arrays for the tradeoff front. These arrays are further analyzed in terms of sustainability, cumulative exergy loss (i.e. irreversibilities and waste exergy) and incremental economic cost, and the results are compared with the goals of current legislated energy policy.