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A major ongoing effort by utilities is to improve their emergency preparedness process for weather events, in order to: 1) reduce outage time 2) reduce repair and restoration costs and 3) improve customer satisfaction. This paper proposes a method for forecasting the number of damages of different types that will result from a weather event, up to 3 days before the event actually occurs. The proposed...
Wind power penetration in power systems is significantly increasing over the years. Wind generation is highly random and a significant change in wind power within a short timeframe forms a wind ramp event. These events can create severe generation-demand imbalance and cause damage to the wind turbines due to extreme stresses. Therefore, prediction of wind ramp events is essential for system operators...
Accurate solar power forecasting allows utilities to get the most out of the solar resources on their systems. To truly measure the improvements that any new solar forecasting methods can provide, it is important to first develop (or determine) baseline and target solar forecasting at different spatial and temporal scales. This paper aims to develop baseline and target values for solar forecasting...
Uncertainty in power output of photovoltaic (PV) plants has raised concerns in regard to the interconnection of PV plants to the power systems. Climate conditions vastly affect the uncertainty of output power; however, aggregation of PV plants is potentially able to reduce the uncertainty; hence, the effectiveness of aggregation should be evaluated for climatically diverse locations. In this paper,...
Due to the variability of wind power, it is imperative to accurately and timely forecast wind generation to enhance the flexibility and reliability of the operation and control of real-time power systems. Special events such as ramps and spikes are hard to predict with traditional methods using solely recently measured data. In this paper, a new Gaussian Process model with hybrid training data taken...
The paper considers stochastic optimization of the electricity procurement in the day-ahead power market. The novelty is in addressing the random errors of time series forecasting of electrical power loads and prices in the procurement. This problem is currently important because of the increased random variability in the power grid that is caused by growing integration of renewable generation. This...
This paper presents a novel detrending algorithm that allows long-term natural gas demand signals to be used effectively to generate high quality short-term natural gas demand forecasting models. Short data sets in natural gas forecasting inadequately represent the range of consumption patterns necessary for accurate short-term forecasting. In contrast, longer data sets present a wide range of customer...
This paper introduced a novel forecasting method, Support Vector Regression with Local Predictor (SVRLP), which aims to forecast the short-term load distribution function. To increase the forecast accuracy, the conventional Support Vector Regression (SVR) is combined with a phase space reconstruction technique, called local predictor. This proposed forecast method can be applied to forecast the load...
The Static Security Assessment of power system is affected by the uncertainty of the power flow distribution introduced by renewable resources. In this paper, a fast contingency selection approach considering load and generation uncertainties is proposed. Firstly, a linearized interval power flow algorithm is developed for post-contingency simulation. Then, an interval number comparison method based...
Due to the growth in the number of residential photo voltaic (PV) adoptions in the past five years, there is a need in the electricity industry for a widely-accessible model that predicts the adoption of PV based on different business and policy decisions. We analyze historical adoption patterns and find that monetary savings is the most important factor in the adoption of PV, superseding all socioeconomic...
This paper proposes a hybrid short-term load forecasting (STLF) framework with a new, more efficient, input selection method. Correlation analysis and ℓ2-norm are used in combination to select suitable inputs to individual Bayesian neural networks (BNNs), which are used to forecast the load. Forecast outputs are then weighted using calculated weighting coefficients and summed to obtain the final forecast...
Wind power forecasting is one of the most important aspects for power system with integration of wind power. In this work, Component GARCH-M (CGARCH-M) model is presented for short-term wind power forecasting (STWPF). Moreover, asymmetric and distributional considerations are taken into account to generalize the CGARCH-M type models. The CGARCH-M type models can decompose the volatility structure...
The worldwide increase in the integration of photovoltaic generation has necessitated improvements in the forecasting approaches. Two models are proposed to cater for PV generation forecasts for few minutes to several hours look-ahead times. A very fast and accurate prediction model based on extreme learning machine is deployed for day-ahead prediction. Moreover, an adaptive and sequential model is...
As part of the unified Smart Automatic Generation Control (SAGC) solution framework, the Unit Response and Unit Tuning functional block plays an important role in achieving desired control performance. While the other functional blocks such as Very Short Term Load Prediction (VSTLP), Predictive Economic Dispatch (PED) and Predictive CPS Control, work together to compute the generating unit's economic...
Quantification of uncertainties associated with solar photovoltaic (PV) power generation forecasts is essential for optimal management of solar PV farms and their successful integration into the grid. These uncertainties can be appropriately quantified and represented in the form of probabilistic rather than deterministic. This paper introduces bootstrap confidence intervals (CIs) to quantify uncertainty...
Nonlinearity of power flow equations is one of the major underlying factors in a power systems operation complexity. The need for a robust and less complex models rises in a volatile, dynamic and real time scenario. This paper introduces new empirical models using multivariate linear regression (MLR) methods with least squares for both real and reactive branch flows. The models do not make prior assumptions...
A novel model predictive control (MPC) scheme is developed for mitigating the effects of severe line-overload disturbances in electrical power systems. A piece-wise linear convex approximation of line losses is employed to model the effect of transmission line power flow on conductor temperatures. Control is achieved through a receding-horizon model predictive control (MPC) strategy which alleviates...
Electric transportation is one of the key elements of the future power systems since conventional power networks are rapidly evolving towards smart grids. This transition creates the need for systematic utilization of electric vehicles (EV) in order to avoid unpredictable effects on the power systems. An accurate and efficient method for demand forecasting of EVs is needed to perform a feasible scheduling...
This paper proposes a method for estimate the percentage annual failure rate of wind turbine electrical components due to the exposure to lightning flashes. It is well known that modern wind turbines are exposed to lightning strikes due to their locations and tall structures (in excess of 100 m high). However, the methodology for estimating the failure rate of the electrical components is not well...
There are many uncertainties associated with forecasting electric vehicle charging and discharging capacity due to the stochastic nature of human behavior surrounding usage and intermittent travel patterns. This uncertainty if unmanaged has the potential to radically change traditional load profiles. Therefore optimal capacity forecasting methods are important for large-scale electric vehicle integration...
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