Computation of loop flows in electric grids with high wind energy penetration

2013 Fall.Includes bibliographical references.In a deregulated electricity market, the financial transmission rights (FTRs) and the bid-sell principle for energy trades are used to determine the expected power flows on transmission lines. Expected power flows are calculated by applying the superposition theorem on the approved electronic tags (e-tags). Multiple parallel paths in interconnected networks lead to division of power flows determined by the impedances of the parallel paths and the physical laws of electricity. The actual power flows in the network do not conform to the market expectations leading to unscheduled flows (USF) on transmission lines. USF have historically been estimated and accommodated deterministically for a given set of e-tags. However, wide-area interconnections experience variability and uncertainty due to a significant penetration of wind energy connected at the transmission level, thus imparting a stochastic nature to USF. A linear model, from the literature, has been adopted to model USF using a mathematical artifact called `minor loop flows'. This research develops an automated framework that provides accurate estimates of loop flows suitable for both market and network level accommodation of variable USF. This generic framework will be applicable to any power transmission network with intermittent energy resources. A loop detection algorithm (LDA) based on graph theory is proposed to detect loops in a transmission network of any size. The LDA is formulated as a modification of the A-star (A*) algorithm, the lowest ancestor theorem, and Dijkstra's algorithm. The LDA has an order of complexity of V2, where V is the total number of vertices or buses in the network under consideration. An application of a geographical information systems (GIS) technique has been established to obtain the transmission line layouts. The outcome of the LDA (i.e., minor loops) and line layouts (i.e., azimuth) are processed to compute the incidence matrix of the estimator. The variability due to the penetration of wind energy is accounted in the proposed framework using the probabilistic load flow analysis based on Monte Carlo simulations. Three techniques - ordinary least squares (OLS), analytic ridge regression (RR), and robust regression (M-estimators) - are used to estimate minor loop flows. The estimation techniques adhere to the auto-correction of the quality of estimates in case of ill-conditioning of the incidence matrix. Accuracy of loop flow estimates is highly significant, as they may be used for assigning economic responsibility of USF in electricity markets. Wind power generation companies (WGENCOs) employ forecasting models to participate in the primary electricity markets. Forecasting models used to predict the output of wind power plants are inherently erroneous and hence, their impacts on USF are studied. The impact of forecasting errors associated with the output of wind plants is investigated using the concept of prediction intervals rather than point accurate forecasts. Loop flow estimates corresponding to the prediction intervals of power output of wind power plants are computed to provide statistical bounds. The proposed framework is tested on the IEEE 14-bus and the IEEE 30-bus standard test systems with suitable modifications to represent wind energy penetration. Accurate loops are detected for the aforementioned test systems using the LDA. Thus, an automated and generic computation of loop flows is proposed along with a step-wise demonstration on IEEE test systems is provided. Future work and concluding remarks summarize the research work in this dissertation

Uncertainty quantification for wind farm power generation

Accurate forecasting of wind farm power generation is essential for successful operation and management of wind farms and to minimize risks associated with their integration into energy systems. However, due to the inherent wind intermittency, wind power forecasts are highly prone to error and often far from being perfect. The purpose of this paper is to develop statistical methods for quantifying uncertainties associated with wind power generation forecasts. Prediction intervals (PIs) with a prescribed confidence level are constructed using the delta and bootstrap methods for neural network forecasts. The moving block bootstrap method is applied to preserve the correlation structure in wind power observations. The effectiveness and efficiency of these two methods for uncertainty quantification is examined using two month datasets taken from a wind farm in Australia. It is demonstrated that while all constructed PIs are theoretically valid, bootstrap PIs are more informative than delta PIs, and are therefore more useful for decision-making.<br /