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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...
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...
This paper presents the implementation of a Model Predictive Control (MPC) strategy for integration of solar PV generation with batteries on an active power distribution system, i.e. micro-grid. Here, the discrete-time finite-horizon optimal control problem associated to the MPC is presented as a non- convex optimization problem. Then, this control problem is solved by utilization of a convexification...
Wind intermittency is the main obstacle to wider integration of wind generation. Jay Apt et. al. claim that wind power generation is so unreliable that 100% of scheduled wind generation must be backed up by spinning non-renewable generators. We believe that the requirements for spinning reserve can be reduced by effective short-term forecasting on both spatial and temporal information. Although similar...
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...
A growing amount of variable generation is being connected to the distribution network, where it is not directly monitored or controlled by the system operator. This introduces a greater degree of uncertainty into bulk system operations. For short-term operational planning tasks, such as the prediction of network congestions, it is becoming increasingly necessary to forecast demand/generation profiles...
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...
Wind power is uncertain and fluctuating. To address its impact on system, a jointed scheduling model of wind power/thermal generation/ energy storage system (ESS) is established in this paper, based on bi-level programming (BLP). Operations of different types of power sources are optimized in the upper-level, while unit commitment of thermal generation is optimized in the lower-level. These two layers...
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...
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...
This paper presents control algorithms and sizing strategies for using energy storage to manage energy imbalance for variable generation resources. The control objective is to minimize the hourly generation imbalance between the actual and the scheduled generation of wind farms. Three control algorithms are compared: 1) tracking minute-by-minute power imbalance; 2) postcompensation; and 3) precompensation...
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...
Wind power ramp events (WPREs) have received increasing attention in recent years due to their significant impact on the reliability of power grid operations. In this paper, a novel WPRE forecasting method is proposed which is able to estimate the probability distributions of three important properties of the WPREs. To do so, a neural network (NN) is first proposed to model the wind power generation...
Ultra-short-term forecasting results of the loads of distribution transformers are one of the main sources of the pseudo measurements in state estimation programs for distribution networks, and the forecasting accuracy seriously affects the state estimation results. This paper describes a robust exponentially weighted load forecast model to improve the forecasting accuracy. Firstly, a load change...
Significant wind power ramp events (WPREs) are those that influence the integration of wind power, and they are a concern to the continued reliable operation of the power grid. As wind power penetration has increased in recent years, so has the importance of wind power ramps. In this paper, an optimized swinging door algorithm (SDA) is developed to improve ramp detection performance. Wind power time...
In electricity markets, credit collateral requirements for participants have traditionally been set based on historical price data that may not properly reflect future risks. A new predictive approach to determining credit risk is proposed in this paper. For any market that prices reserves in the real-time market, correlation exists between available reserve levels and real-time energy prices. This...
Probabilistic forecasting provides quantitative information of energy uncertainty, which is very essential for making better decisions in power system operation with increasing penetration of wind power and solar power. On the basis of k-nearest neighbor and kernel density estimator method, this paper presents a general framework of probabilistic forecasts for renewable energy generation. Firstly,...
In this paper, three individual indices, as well as a new comprehensive index, are introduced to evaluate prediction intervals. Then, two practical methods, namely, Interval Extension Method and Optimal Scalar Method are proposed to build the prediction intervals based on an ensemble of Extreme Learning Machines. Case studies on hour-ahead load interval forecasting with respect to Chicago Metro Area...
Short-term wind farm power forecasting is studied by exploiting the spatio-temporal correlation between individual turbine's power output. A multivariate time series model for wind farm power generation is developed by using vector autoregression (VAR). In order to avoid the possible over-fitting issues caused by a large number of autoregressive coefficients and the impact on the forecasting performance...
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