Embedded wind generation in weak grids—economic optimisation and power quality simulation

IEEE Trans. Power Syst.

Domínguez-Navarro

Llombart

2008

655

337

Wind power forecast error usually has been assumed to have a near Gaussian distribution. With a simple statistical analysis, it can be shown that this is not valid. To obtain a more appropriate probability density function (pdf) of the wind power forecast error, an indirect algorithm based on the Beta pdf is proposed. Measured one-year time series from two different wind farms are used to generate the forecast data. Three different forecast scenarios are simulated based on the persistence approach. This makes the results comparable to other forecast methods. It is found that the forecast error pdf has a variable kurtosis ranging from 3 (like the Gaussian) to over 10, and therefore it can be categorized as fat-tailed. A new approximation function for the parameters of the Beta pdf is proposed because results from former publications could not be confirmed. Besides, a linear approximation is developed to describe the relationship between the persistence forecast and the related mean measured power. An energy storage system (ESS), which reduces the forecast error and smooths the wind power output, is considered. Results for this case show the usefulness of the proposed forecast error pdf for finding the optimum rated ESS power.

“…Following [17], it can be written as follows: (2) where is the prediction delay, is the wind power forecast for time for the prediction made at the origin and is the measured wind power.…”

Section: A Modeling Forecast Scenarios Using Persistencementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Statistical Analysis of Wind Power Forecast Error

IEEE Trans. Power Syst.

Domínguez-Navarro

Llombart

2008

655

337

“…According to the nomenclature proposed in [16], the persistence forecast can be written as (1) where is the wind power forecast for time made at time origin , is the measured wind power, the prediction horizon, the number of time steps within prediction period , and the time step length of the measured time series . Three different forecast qualities were simulated in [13], creating three forecast scenarios.…”

Section: B Modeling Forecast Scenarios Using Persistencementioning

confidence: 99%

“…I N recent years, wind energy has grown to an extent that its impact on the electric power system becomes relevant in many regions [1], [2]. On the other side, generation costs are falling and the participation in short-term energy markets becomes interesting [3]- [5].…”

Section: Introductionmentioning

confidence: 99%

A Probabilistic Method for Energy Storage Sizing Based on Wind Power Forecast Uncertainty

IEEE Trans. Power Syst.

Domínguez-Navarro

2011

227

112

A novel method is proposed for designing an energy storage system (ESS) which is dedicated to the reduction of the uncertainty of short-term wind power forecasts up to 48 h. The investigation focuses on the statistical behavior of the forecast error and the state of charge (SOC) of the ESS. This approach gives an insight into the influence of the forecast conditions (horizon, quality) on the distribution of SOC. With this knowledge, an optimized sizing of the ESS can be done with a well-defined uncertainty limit. For this study, one-year time series of power output measurements and forecasts were available for two wind farms. As a reference, different forecast quality degrees are simulated based on a persistence approach. With the forecast data, empirical probability density functions (pdfs) are generated which are the basis of the proposed method. This approach can lead to a considerable reduction of the ESS and provides important information about the unserved energy. This unserved energy represents the remaining forecast uncertainty. As a consequence, the proposed probabilistic method permits the sizing of energy storage systems as a function of the desired remaining forecast uncertainty, reducing simultaneously power and energy capacity.Index Terms-Energy storage sizing, probability density function, short-term forecast error, state of charge, wind power.

“…In recent years wind energy has grown to an extent that its impact on the electric power system becomes relevant in many regions [1]- [3]. On the other side, generation costs are falling and the participation in short term energy markets becomes interesting [4]- [7].…”

Section: Introductionmentioning

confidence: 99%

Probabilistic energy storage sizing for reducing wind power forecast uncertainty

Domínguez

2024

RE&PQJ

Abstract.A novel method is proposed for designing an energy storage system (ESS) which is dedicated to reducing the uncertainty of the short term wind power forecast. The investigation focuses on the statistical behaviour of the forecast error and the state of charge (SOC) of the ESS. This approach gives an insight into the influence of the forecast conditions on the distribution of SOC. With this knowledge, an optimised sizing of the ESS can be done with a well defined uncertainty limit. One-year power output data measurements and two types for forecast were used for this study. In addition, different forecast quality degrees are simulated based on the persistence approach. With the forecast data, empirical probability density functions (pdf's) of the SOC are generated which is the base of the proposed method. This approach can lead to a considerable reduction of the ESS and provides important information about the unserved energy. This unserved energy is the remaining forecast error or uncertainty. As a consequence, the proposed probabilistic method permits the sizing of the energy storage system as a function of the desired remaining forecast uncertainty.