Adaptive Robust Optimization with Dynamic Uncertainty Sets for Multi-Period Economic Dispatch under Significant Wind

The exceptional benefits of wind power as an environmentally responsible renewable energy resource have led to an increasing penetration of wind energy in today's power systems. This trend has started to reshape the paradigms of power system operations, as dealing with uncertainty caused by the highly intermittent and uncertain wind power becomes a significant issue. Motivated by this, we present a new framework using adaptive robust optimization for the economic dispatch of power systems with high level of wind penetration. In particular, we propose an adaptive robust optimization model for multi-period economic dispatch, and introduce the concept of dynamic uncertainty sets and methods to construct such sets to model temporal and spatial correlations of uncertainty. We also develop a simulation platform which combines the proposed robust economic dispatch model with statistical prediction tools in a rolling horizon framework. We have conducted extensive computational experiments on this platform using real wind data. The results are promising and demonstrate the benefits of our approach in terms of cost and reliability over existing robust optimization models as well as recent look-ahead dispatch models.Comment: Accepted for publication at IEEE Transactions on Power System

An Unsupervised Deep Learning Approach for Scenario Forecasts

In this paper, we propose a novel scenario forecasts approach which can be applied to a broad range of power system operations (e.g., wind, solar, load) over various forecasts horizons and prediction intervals. This approach is model-free and data-driven, producing a set of scenarios that represent possible future behaviors based only on historical observations and point forecasts. It first applies a newly-developed unsupervised deep learning framework, the generative adversarial networks, to learn the intrinsic patterns in historical renewable generation data. Then by solving an optimization problem, we are able to quickly generate large number of realistic future scenarios. The proposed method has been applied to a wind power generation and forecasting dataset from national renewable energy laboratory. Simulation results indicate our method is able to generate scenarios that capture spatial and temporal correlations. Our code and simulation datasets are freely available online.Comment: Accepted to Power Systems Computation Conference 2018 Code available at https://github.com/chennnnnyize/Scenario-Forecasts-GA

A CFD framework for offshore and onshore wind farm simulation

We present a wind simulation framework for offshore and onshore wind farms. The simulation framework involves an automatic hybrid high-quality mesh generation process, a pre-processing to impose initial and boundary conditions, and a solver for the Reynolds Averaged Navier-Stokes (RANS) equations with two different turbulence models, a modified standard k-epsilon model and a realizable k-epsilon model in which we included the Coriolis effects. Wind turbines are modeled as actuator discs. The wind farm simulation framework has been implemented in Alya, an in-house High Performance Computing (HPC) multi-physics finite element parallel solver. An application example is shown for an onshore wind farm composed of 165 turbines.This work has been supported by the EU H2020 projects New European Wind Atlas ERA-NET PLUS (NEWA), High Performance Computing for Energy (HPC4E, grant agreement 689772), and the Energy oriented Centre of Excellence (EoCoE, grant agreement 676629). We also thank two anonymous reviewers for their constructive comments on the first version of the manuscript.Peer ReviewedPostprint (published version

Using conditional kernel density estimation for wind power density forecasting

Of the various renewable energy resources, wind power is widely recognized as one of the most promising. The management of wind farms and electricity systems can benefit greatly from the availability of estimates of the probability distribution of wind power generation. However, most research has focused on point forecasting of wind power. In this paper, we develop an approach to producing density forecasts for the wind power generated at individual wind farms. Our interest is in intraday data and prediction from 1 to 72 hours ahead. We model wind power in terms of wind speed and wind direction. In this framework, there are two key uncertainties. First, there is the inherent uncertainty in wind speed and direction, and we model this using a bivariate VARMA-GARCH (vector autoregressive moving average-generalized autoregressive conditional heteroscedastic) model, with a Student t distribution, in the Cartesian space of wind speed and direction. Second, there is the stochastic nature of the relationship of wind power to wind speed (described by the power curve), and to wind direction. We model this using conditional kernel density (CKD) estimation, which enables a nonparametric modeling of the conditional density of wind power. Using Monte Carlo simulation of the VARMA-GARCH model and CKD estimation, density forecasts of wind speed and direction are converted to wind power density forecasts. Our work is novel in several respects: previous wind power studies have not modeled a stochastic power curve; to accommodate time evolution in the power curve, we incorporate a time decay factor within the CKD method; and the CKD method is conditional on a density, rather than a single value. The new approach is evaluated using datasets from four Greek wind farms

On the Use of Acoustic Wind Tunnel Data for the Simulation of sUAS Flyover Noise

Acoustic measurements of a small, unmanned aerial system were recently acquired during a ground test campaign. The purposes of the ground test, conducted in the NASA Langley Low Speed Aeroacoustic Wind Tunnel, were to characterize the source noise in terms of its tonal and broadband content, and to identify conditions under which multirotor and rotor-airframe interactions are present. The focus of this work is to assess the effectiveness of using those data for the simulation of flyover noise at a ground observer. The assessment is made at two levels of fidelity using different sets of tools. In the first, 1/3 octave band spectra at a ground receiver will be simulated in a frequency domain approach using the NASA Aircraft NOise Prediction Program. In the second, the pressure time history at a ground receiver is simulated in a time domain approach using the NASA Auralization Framework. Various objective measures are used to verify the simulation process. Acoustic wind tunnel and flight test data are used to gain insight into perceptually important effects

Modelling the spreading of wilding conifers

In many parts of the Canterbury high country, conifer seeds are spreading on the wind from exisiting plantations and shelterbelts, leading to a serious weed problem. Environment Canterbury set the task at MISG to model this spread, and thus provide a basis for prioritising control operations on a limited budget. The study group provided increased understanding of topographic and climatic factors involved in seed dispersal, and of the distribution of the resulting seed rain. In addition a simulation framework was developed for comparing the effectiveness of different control strategies