73 research outputs found
Capturing Distribution Grid-Integrated Solar Variability and Uncertainty Using Microgrids
The variable nature of the solar generation and the inherent uncertainty in
solar generation forecasts are two challenging issues for utility grids,
especially as the distribution grid integrated solar generation proliferates.
This paper offers to utilize microgrids as local solutions for mitigating these
negative drawbacks and helping the utility grid in hosting a higher penetration
of solar generation. A microgrid optimal scheduling model based on robust
optimization is developed to capture solar generation variability and
uncertainty. Numerical simulations on a test feeder indicate the effectiveness
of the proposed model.Comment: IEEE Power and Energy Society General Meeting, 201
An ANN-based Approach for Forecasting the Power Output of Photovoltaic System
AbstractWith the increasing use of large-scale grid-connected photovoltaic system, accurate forecast approach for the power output of photovoltaic system has become an important issue. In order to forecast the power output of a photovoltaic system at 24-hour-ahead without any complex modeling and complicated calculation, an artificial neural network based approach is proposed in this paper. The improved back-propagation learning algorithm is adopted to overcome shortcomings of the standard back-propagation learning algorithm. Similar day selection algorithm based on forecast day information is proposed to improve forecast accuracy in different weather types. Forecasting results of a photovoltaic system show that the proposed approach has a great accuracy and efficiency for forecasting the power output of photovoltaic system
Bayesian rules and stochastic models for high accuracy prediction of solar radiation
It is essential to find solar predictive methods to massively insert
renewable energies on the electrical distribution grid. The goal of this study
is to find the best methodology allowing predicting with high accuracy the
hourly global radiation. The knowledge of this quantity is essential for the
grid manager or the private PV producer in order to anticipate fluctuations
related to clouds occurrences and to stabilize the injected PV power. In this
paper, we test both methodologies: single and hybrid predictors. In the first
class, we include the multi-layer perceptron (MLP), auto-regressive and moving
average (ARMA), and persistence models. In the second class, we mix these
predictors with Bayesian rules to obtain ad-hoc models selections, and Bayesian
averages of outputs related to single models. If MLP and ARMA are equivalent
(nRMSE close to 40.5% for the both), this hybridization allows a nRMSE gain
upper than 14 percentage points compared to the persistence estimation
(nRMSE=37% versus 51%).Comment: Applied Energy (2013
Photovoltaic power forecasting with a rough set combination method
One major challenge with integrating photovoltaic (PV) systems into the grid is that its power generation is intermittent and uncontrollable due to the variation in solar radiation. An accurate PV power forecasting is crucial to the safe operation of the grid connected PV power station. In this work, a combined model with three different PV forecasting models is proposed based on a rough set method. The combination weights for each individual model are determined by rough set method according to its significance degree of condition attribute. The three different forecasting models include a past-power persistence model, a support vector machine (SVM) model and a similar data prediction model. The case study results show that, in comparison with each single forecasting model, the proposed combined model can identify the amount of useful information in a more effective manner
Two-Stage Hybrid Day-Ahead Solar Forecasting
Power supply from renewable resources is on a global rise where it is
forecasted that renewable generation will surpass other types of generation in
a foreseeable future. Increased generation from renewable resources, mainly
solar and wind, exposes the power grid to more vulnerabilities, conceivably due
to their variable generation, thus highlighting the importance of accurate
forecasting methods. This paper proposes a two-stage day-ahead solar
forecasting method that breaks down the forecasting into linear and nonlinear
parts, determines subsequent forecasts, and accordingly, improves accuracy of
the obtained results. To further reduce the error resulted from nonstationarity
of the historical solar radiation data, a data processing approach, including
pre-process and post-process levels, is integrated with the proposed method.
Numerical simulations on three test days with different weather conditions
exhibit the effectiveness of the proposed two-stage model
- …