Discipline: Mathematics and Statistics
Sukhmeni Chauhan - California State University of Bakersfield
Wind energy is a renewable resource that has the potential to replace our dependence on fossil fuels such as coal, oil, and natural gas. Despite all the benefits, sustainable integration of wind as a resource is problematic due to their intermittent nature. For instance, in an unprecedented event in Texas in February 2008, a large amount of wind power was lost due to an unforeseen weather change, which caused a substantial increase in energy prices in the Dallas/Forth Worth area . Besides, with high amount of wind penetration in electric grid, line voltage fluctuations can occur due to rapid changes in wind power generation, and in particular, in areas where wind generation is geographically concentrated. As a result, voltage regulation devices have to operate more frequently and can wear out faster than expected. A well-known solution to these problems is installation of distributed reserve capacities such as networked (grid-connected) user batteries to help the utility stabilize the grid during erratic wind conditions . But necessary infrastructure for this solution is not in place and developing appropriate technologies may take years. Moreover, even if such solution existed, it only could partially alleviate the intermittency of wind resources. Therefore, forecasting the available wind energies is necessary and can further facilitate their integration. This research study concentrates on the forecast of wind electricity generation by means of Markov chains. In this study, we presented a novel approach in using Markov chains in wind power forecast. The main innovative advantage is the enhancement in parts of the model where a lack of data causes inaccuracy problems. The proposed methodology does not require additional data or structural change; instead bootstrap resampling is utilized to evaluate the accuracy in model parameters. We then used an innovative probabilistic interpolation to improve the weak elements of the Markov chain. Our simulation results using real wind power data proved significant improvement in the accuracy of the forecast. The future work will focus on using the proposed approach to develop power system state forecasting.
References: 1.M. Lu, C. Chang, W. Lee, and L. Wang, ‘Combining the wind power generation system with energy storage equipment,’ In IEEE Industry Applications Society Annual Meeting, 2008.
2.M. Bragard, N. Soltau, S. Thomas, and R. W. De Doncker, ‘The balance of renewable sources and user demands in grids: Power electronics for modular battery energy storage systems,’ IEEE Trans. on Power Electronics, vol. 25, no. 12, pp. 3049-3056, 2010.
Funder Acknowledgement(s): This material is based upon work supported by, or in part by, the U. S. Army Research Laboratory and the U. S. Army Research Office under contract/grant number W911NF1510498.
Faculty Advisor: Saeed Jafarzadeh, email@example.com
Role: To improve the accuracy of wind power forecast I took data given from BPA (Bonneville Power Administration) website for wind generation. Given the data I was able to use bootstrap resampling to determine the accuracy of the data. I then used a method of weighted interpolation to determine which parts of the data should be weighted more than others. This allowed the accuracy of the data to be improved, allowing me to make a probability transition matrix(PTM) with an improvement in accuracy.