Characterization and Optimal Site Matching of Wind Turbine: Effects on the Economics of Synthetic Methane Production

Wind can easily provide the energy that mankind needs. However, harvesting energy from wind is challenging. There are only a few regions where it is economically interesting to use it. Furthermore, the variability in the wind velocity and the particular design of the turbine determine the turbine model to use for a particular site location based on its characteristic power curve. Instead of just producing power, with the challenge of its availability, it is possible to store wind energy in the form of chemicals. Davis and Martín (2014a&b) used solar and wind energy to produce methane from renewable energy and CO₂ not only to store both sources of energy in a more handy form, but also to capture and reuse CO₂ as a carbon source. However, the design of such processes must consider seasonal wind variability and uncertainty (Halemane and Grossmann (1983), Grossmann and Sargent (1978)). Thus, the selection of the appropriate turbine type for a specific site location requires accurate modeling of the power curve to address its operation under variable wind velocity conditions. Previous studies on turbine site –matching are based on the capacity factor, using piecewise approximations of the power curve to obtain analytical solutions Jowder (2009) El-Shiny (2010). A more systematic study and power curve modeling is required.

In this work, we evaluate the optimal site matching of wind farms developing a generic single equation model to characterize the power curve of a number of current commercial onshore and offshore wind turbines. Next, mathematical MINLP formulations are developed that allow considering the velocity distribution on a monthly basis, including wind variability using Weibull distribution. We apply the formulation to select the appropriate turbine type and number for the production of synthetic methane. This allows evaluating the effect of the site on the wind turbine selection and the investment and production costs of synthetic methane from hydrolytic hydrogen and CO₂. The chemical facility was developed in a previous work Davis and Martín (2014a&b). In that work it was shown that 94% of the investment cost is due to the wind farm. Thus, we can identify advantages in selecting the proper place and turbine in terms of economic savings for producing an easy to use and store/transport fuel.

The optimal selection of turbine and place result in competitive prices for synthetic methane, below 2€/MMBTU, as long as the appropriate turbine and allocation are selected. By using the formulation to select the allocation of such a facility in Spain, the best place selected corresponds to the one in Davis and Martín (2014a) paper, Cádiz. However, the use of the appropriate turbine design resulted in savings of 42 MM€ compared to the results in Davis and Martín (2014a) and a production cost 25% lower. Therefore, the formulation presented in this work provides a useful tool for the selection of turbines and allocations. Finally, the selection under uncertainty results in the fact that, although in general the cost is expected to be higher, since a more robust solution is expected, sometimes lower production prices are found due to the fact that the more detailed wind distribution fits better with the power curve than just an average velocity.

References

Archer CL., 2004. The Santa Cruz Eddy and U.S. wind power, Ph.D. Thesis, 190 pp., Stanford University,

Stanford, 1 April 2004.

Davis, W., Martín, M ., 2014a. Optimal year-round operation for methane production from CO₂ and Water using wind energy. Energy 69, 497-505

Davis, W., Martín, M., 2014b. Optimal year-round operation for methane production from CO₂ and Water using wind and/or Solar energy. J. Cleaner Prod. 80, 252-261.

El-Shimy, M. Optimal site matching of wind turbine generator: Case study of the Gulf of Suez region in Egypt. Renew. Energ. 35 (8), 1870-1878

Grossmann, I. E., Sargent, R.W.H., 1978. Optimum Design of Chemical Plants with Uncertain Parameters, AIChE J., 24 (6), 1021-1028

Halemane, K.P.,Grossmann, I. E. , 1983. Optimal process design under uncertainty AIChE, J., 29 (3), 425-433