Discipline: Technology and Engineering
Subcategory: Electrical Engineering
Cecilia Osorio - California State University, Bakersfield
Renewable energy can serve as a viable alternative for fossil fuels and offer great environmental advantages including carbon footprint reduction. The two forms of renewable resources that are commonly used for electric power generation are wind and solar resources. However, the use of these resources in electric grid can be problematic due to their intermittency. Due to the small storage capacity of the grid, the electricity delivered from the suppliers to the consumers must be balanced at all times . For this reason, electric power engineers use several optimization studies to plan the load and generation ahead of time. These studies include optimal power flow, economic dispatch, and unit commitment. In this research study, we concentrate on the impact of solar and wind electric energy generation on optimization studies by reconsidering the source of their intermittency: uncertainty in electricity generation. Regardless of their sources, uncertainties are classified into two major classes: vagueness and randomness. Vagueness often results in linguistic uncertainties, and randomness results in stochastic uncertainties . Solar and wind energy power generation typically involves meteorological quantities, which contain high levels of linguistic uncertainties . The well-known mathematical tool for addressing linguistic uncertainties is fuzzy logic. Hence, we used fuzzy logic to enhance a commonly used optimization methodology, Particle Swarm Optimization (PSO). PSO is a population-based method that optimizes a problem by focusing on the parameters required to maximize or minimize the objective problem. In this study, we reinvented PSO by considering fuzzy uncertainties in the underlying optimization problem. The results can be applied to power systems operations, and therefore can have a significant impact on implementation of higher levels of renewable energies on electric grid. The future work is to apply the developed methodology on various operational studies for power systems.
References:  Fares, Robert. ‘Renewable Energy Intermittency Explained: Challenges, Solutions, and Opportunities.’ Scientific American, March 11 (2015).
 H. R. Berenji, ‘Treatment of uncertainty in artificial intelligence,’ Machine Intelligence and Autonomy Aerospace Systems, 1988.
 S. Jafarzadeh, M. S. Fadali and C. Y. Evrenosoglu, ‘Solar power prediction using interval type-2 TSK modeling,’ ‘IEEE Trans. on Sustainable Energy,’ vol. 4, no. 2, pp. 333-339, 2013.
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, firstname.lastname@example.org
Role: I did all of this research. I concentrated on the impact of solar and wind electric energy generation on optimization studies and considered fuzzy uncertainties in the underlying optimization problem.