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

Ohio State University, The, Columbus OH

Investigators

Abstract

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 order micro 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.