Jie Zhang

TL;DR: In this paper, a new methodology, the Unrestricted Wind Farm Layout Optimization (UWFLO), is presented to address critical aspects of optimal wind farm planning, simultaneously determining the optimum farm layout and the appropriate selection of turbines (in terms of their rotor diameters) that maximizes the net power generation.

TL;DR: Numerical results show that comparing to the single-algorithm models, the developed multi-model framework with deep feature selection procedure has improved the forecasting accuracy by up to 30%.

TL;DR: In this paper, an advanced version of the Unrestricted wind farm layout optimization (UWFLO) method is proposed to simultaneously optimize the placement and the selection of turbines for commercial-scale wind farms that are subject to varying wind conditions.

TL;DR: A suite of generally applicable and value-based metrics for solar forecasting for a comprehensive set of scenarios that were developed as part of the U.S. Department of Energy SunShot Initiative's efforts to improve the accuracy of solar forecasting show that the proposed metrics can efficiently evaluate the quality of solar forecasts and assess the economic and reliability impacts of improved solar forecasting.

TL;DR: In this paper, a neural network is proposed to estimate the probability distributions of three important properties of wind power ramp events (WPREs) by modeling the wind power generation as a stochastic process so that a number of scenarios of the future WPG can be generated or predicted.