CAREER: An Integrated Hybrid Forecasting Framework for Increased Wind Power Penetration

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The goal of the proposed research is to develop the next generation of algorithms to achieve significant improvement in short-term wind forecasting. Our inability to accurately capture non-Gaussian uncertainty of wind in high dimensional spaces translates to low predictability; high risk and the need for expensive balancing power resources. It is thus a primary objective of the proposed research is to significantly improve wind forecasts upto 48 hours in advance, leading to enhanced dispatch, scheduling and unit commitment operations in the day-ahead electricity market.Intellectual Merit: The proposed research will develop an integrated framework of randomized algorithms for scalable nonlinear uncertainty propagation. Algorithm output will be combined with measured on-site data in the sense of Bayesian fusion, leading to a hybrid forecasting structure. The main technical challenges are: (i) complex wind dynamics due to multiple temporal and spatial scales, turbulence and orographic effects; (ii) non-Gaussian wind uncertainty; (iii) need for scalable algorithms due to high dimensionality; and (iv) need for fusion of information arriving from multiple algorithms and heterogeneous measurement sources. The proposed framework will have the following key features to meet these challenges: (1) formulation of the wind state as a stochastic hybrid process, governed by multiple reduced ordermicro and mesoscale models; (2) a novel randomized particle uncertainty propagation approach based on the method of characteristics, Markov chain Monte-Carlo and the Karhunen-Lo`eve expansion. Practical effectiveness of developed algorithms will be measured against data from two wind-farms: the Cohocton Wind Farm/NY and the Roscoe Wind Farm/TX.Broader Impact: The proposed forecasting framework will lead to increased penetration of wind power by reducing the risks currently associated with it; and enable us to achieve our global targets of reducing dependence on fossil-fuel based electricity. The education plan includes training high school teachers in the multidisciplinary area of sustainable energy who will in turn reach thousands of students.