Farshad Harirchi

The participation of renewable energy sources in energy markets is challenging, mainly because of the uncertainty associated with the renewables. Aggregation of renewable energy suppliers is shown to be very effective in decreasing this uncertainty. In the present paper, we propose a cost sharing mechanism that entices the suppliers of wind, solar and other renewable resources to form or join an aggregate. In particular, we consider the effect of a bonus for surplus in supply, which is… Show more

The participation of renewable energy sources in energy markets is challenging, mainly because of the uncertainty associated with the renewables. Aggregation of renewable energy suppliers is shown to be very effective in decreasing this uncertainty. In the present paper, we propose a cost sharing mechanism that entices the suppliers of wind, solar and other renewable resources to form or join an aggregate. In particular, we consider the effect of a bonus for surplus in supply, which is neglected in previous work. We introduce a specific proportional cost sharing mechanism, which satisfies the desired properties of such mechanisms that are introduced in the literature, e.g., budget balancedness, ex-post individual rationality and fairness. In addition, we show that the proposed mechanism results in a stable market outcome. Finally, the results of the paper are illustrated by numerical examples. Show less

In This work, I developed theories for lack of observability issue in run-to-run control and proposed solutions by modifying the Kalman filter algorithm. This work needed a very strong simulation capabilities (Matlab and Python) as well as expertise in system identification, control theory, estimation theory, stochastic processes and data analysis.

In this work one of the main challenges was to register 3D images. In the next step, the features extracted from the MRI images and Hopfield Neural Networks were utilized to mark the pixels that correspond to the skull and scalp of the newborns.

This project also contained image processing and pattern recognition techniques. We segmented lung in HRCT images and detected three types of texture in lung tissue due to the interstitial lung disease. Multiple classifiers have been utilized to use extracted wavelet and curvelet features from the CT images in order to classify the lung tissue into those three texture categories.

Image processing and pattern recognition were two main parts of this project. Denoising, segmentation, feature extraction were components of the image processing side and neural networks, support vector machines and techniques to combine were main components of the pattern recognition side of the work.