49,040 research outputs found
An implementation of synthetic generation of wind data series
Wind power fluctuation is a major concern of large scale wind power grid integration. To test methods proposed for wind power grid integration, a large amount of wind data with time series are necessary and will be helpful to improve the methods. Meanwhile, due to the short operation history of most wind farms as well as limitations of data collections, the data obtained from wind farms could not satisfy the needs of data analysis. Consequently, synthetic generation of wind data series could be one of the effective solutions for this issue. In this paper, a method is presented for generating wind data series using Markov chain. Due to the high order Markov chain, the possibility matrix designed for a wind farm could cost a lot of memory, which is a problem with current computer technologies. Dynamic list will be introduced in this paper to reduce the memory required. Communication errors are un-avoidable on long way signal transmission between the control centre and wind farms. Missing of data always happens in the historical wind data series. Using these data to generate wind data series may result in some mistakes when searching related elements in the probability matrix. An adaptive method will be applied in this paper to solve the problem. The proposed method will be verified using a set of one-year historical data. The results show that the method could generate wind data series in an effective way. © 2013 IEEE.published_or_final_versio
Wind speed forecasting at different time scales: a non parametric approach
The prediction of wind speed is one of the most important aspects when
dealing with renewable energy. In this paper we show a new nonparametric model,
based on semi-Markov chains, to predict wind speed. Particularly we use an
indexed semi-Markov model, that reproduces accurately the statistical behavior
of wind speed, to forecast wind speed one step ahead for different time scales
and for very long time horizon maintaining the goodness of prediction. In order
to check the main features of the model we show, as indicator of goodness, the
root mean square error between real data and predicted ones and we compare our
forecasting results with those of a persistence model
A cyclic time-dependent Markov process to model daily patterns in wind turbine power production
Wind energy is becoming a top contributor to the renewable energy mix, which
raises potential reliability issues for the grid due to the fluctuating nature
of its source. To achieve adequate reserve commitment and to promote market
participation, it is necessary to provide models that can capture daily
patterns in wind power production. This paper presents a cyclic inhomogeneous
Markov process, which is based on a three-dimensional state-space (wind power,
speed and direction). Each time-dependent transition probability is expressed
as a Bernstein polynomial. The model parameters are estimated by solving a
constrained optimization problem: The objective function combines two maximum
likelihood estimators, one to ensure that the Markov process long-term behavior
reproduces the data accurately and another to capture daily fluctuations. A
convex formulation for the overall optimization problem is presented and its
applicability demonstrated through the analysis of a case-study. The proposed
model is capable of reproducing the diurnal patterns of a three-year dataset
collected from a wind turbine located in a mountainous region in Portugal. In
addition, it is shown how to compute persistence statistics directly from the
Markov process transition matrices. Based on the case-study, the power
production persistence through the daily cycle is analysed and discussed
A simulation study of the use of electric vehicles as storage on the New Zealand electricity grid
This paper describes a simulation to establish the extent to which reliance on non-dispatchable energy sources, most typically wind generation, could in the future be extended beyond received norms, by utilizing the distributed battery capacity of an electric vehicle fleet. The notion of exploiting the distributed battery capacity of a nation’s electric vehicle fleet as grid storage is not new. However, this simulation study specifically examines the potential impact of this idea in the New Zealand context. The simulation makes use of real and projected data in relation to vehicle usage, full potential non-dispatchable generation capacity and availability, taking into account weather variation, and typical daily and seasonal patterns of usage. It differs from previous studies in that it is based on individual vehicles, rather than a bulk battery model. At this stage the analysis is aggregated, and does not take into account local or regional flows. A more detailed analysis of these localized effects will follow in subsequent stages of the simulation
The Langevin Approach: An R Package for Modeling Markov Processes
We describe an R package developed by the research group Turbulence, Wind
energy and Stochastics (TWiSt) at the Carl von Ossietzky University of
Oldenburg, which extracts the (stochastic) evolution equation underlying a set
of data or measurements. The method can be directly applied to data sets with
one or two stochastic variables. Examples for the one-dimensional and
two-dimensional cases are provided. This framework is valid under a small set
of conditions which are explicitly presented and which imply simple preliminary
test procedures to the data. For Markovian processes involving Gaussian white
noise, a stochastic differential equation is derived straightforwardly from the
time series and captures the full dynamical properties of the underlying
process. Still, even in the case such conditions are not fulfilled, there are
alternative versions of this method which we discuss briefly and provide the
user with the necessary bibliography
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