Review of Deterministic and Probabilistic Wind Power Forecasting: Models, Methods, and Future Research

Bazionis, Ioannis K.; Georgilakis, Pavlos S.

doi:10.3390/electricity2010002

Cited by 64 publications

(26 citation statements)

References 86 publications

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“…There are studies trying to relate a known PDF to wind generation. They mainly adopted Gaussian and beta distributions to probabilistically forecast wind power using parametric methods [40]. Some research also tried other types of distribution functions.…”

Section: Wind Probabilistic Forecastingmentioning

confidence: 99%

Applications of Probabilistic Forecasting in Smart Grids: A Review

Khajeh

Laaksonen

2022

Applied Sciences

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This paper reviews the recent studies and works dealing with probabilistic forecasting models and their applications in smart grids. According to these studies, this paper tries to introduce a roadmap towards decision-making under uncertainty in a smart grid environment. In this way, it firstly discusses the common methods employed to predict the distribution of variables. Then, it reviews how the recent literature used these forecasting methods and for which uncertain parameters they wanted to obtain distributions. Unlike the existing reviews, this paper assesses several uncertain parameters for which probabilistic forecasting models have been developed. In the next stage, this paper provides an overview related to scenario generation of uncertain parameters using their distributions and how these scenarios are adopted for optimal decision-making. In this regard, this paper discusses three types of optimization problems aiming to capture uncertainties and reviews the related papers. Finally, we propose some future applications of probabilistic forecasting based on the flexibility challenges of power systems in the near future.

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Section: Wind Probabilistic Forecastingmentioning

confidence: 99%

Applications of Probabilistic Forecasting in Smart Grids: A Review

Khajeh

Laaksonen

2022

Applied Sciences

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“…This is particularly important with respect to leading edge erosion, and the drive to develop materials that are more resilient to changing climates. The use of artificial intelligence in forecasting models and probabilistic forecasting could form important decision-making tools in future energy markets (Bazionis and Georgilakis, 2021).…”

Section: Integration Of Weather Forecasting Earth Observation and Climate Changementioning

confidence: 99%

Geoscience Solutions for Sustainable Offshore Wind Development

Velenturf¹,

Emery²,

Hodgson³

et al. 2021

Earth Sci. Syst. Soc.

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Low carbon energy infrastructure, such as wind and solar farms, are crucial for reducing greenhouse gas emissions and limiting global temperature rise to 1.5°C. During 2020, 5.2 GW of offshore wind capacity went into operation worldwide, taking the total operational capacity of global offshore wind to 32.5 GW from 162 offshore windfarms, and over 200 GW of new capacity is planned by 2030. To meet net-zero targets, growth of offshore wind generation is expected, which raises new challenges, including integration of offshore wind into the natural environment and the wider energy system, throughout the wind farm lifecycle. This review examines the role of geosciences in addressing these challenges; technical sustainability challenges and opportunities are reviewed, filtered according to global governance priorities, and assessed according to the role that geoscience can play in providing solutions. We find that geoscience solutions play key roles in sustainable offshore wind energy development through two broad themes: 1) windfarm and infrastructure site conditions, and 2) infrastructure for transmission, conversion and energy storage. To conclude, we recommend priorities and approaches that will support geoscience contributions to offshore wind, and ultimately enable sustainable offshore wind development. Recommendations include industry collaboration and systems for effective data sharing and archiving, as well as further research, education and skills.

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“…Wind speed models based on AR/MA, Markov chain models and other discrete models [10][11][12] do not allow for the variation of simulation time step simulation. This limitation does not allow the use of these models for the simulation of wind power systems with a high degree of time sampling, which is a necessary condition for selecting the optimal configuration of power-generating equipment and analyzing steady-state and transient processes in RES-based electric power systems.…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling Wind Speed Based on Fractional Ornstein-Uhlenbeck Process

et al. 2021

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The primary task of the design and feasibility study for the use of wind power plants is to predict changes in wind speeds at the site of power system installation. The stochastic nature of the wind and spatio-temporal variability explains the high complexity of this problem, associated with finding the best mathematical modeling which satisfies the best solution for this problem. In the known discrete models based on Markov chains, the autoregressive-moving average does not allow variance in the time step, which does not allow their use for simulation of operating modes of wind turbines and wind energy systems. The article proposes and tests a SDE-based model for generating synthetic wind speed data using the stochastic differential equation of the fractional Ornstein-Uhlenbeck process with periodic function of long-run mean. The model allows generating wind speed trajectories with a given autocorrelation, required statistical distribution and provides the incorporation of daily and seasonal variations. Compared to the standard Ornstein-Uhlenbeck process driven by ordinary Brownian motion, the fractional model used in this study allows one to generate synthetic wind speed trajectories which autocorrelation function decays according to a power law that more closely matches the hourly autocorrelation of actual data. In order to demonstrate the capabilities of this model, a number of simulations were carried out using model parameters estimated from actual observation data of wind speed collected at 518 weather stations located throughout Russia.

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Review of Deterministic and Probabilistic Wind Power Forecasting: Models, Methods, and Future Research

Cited by 64 publications

References 86 publications

Applications of Probabilistic Forecasting in Smart Grids: A Review

Applications of Probabilistic Forecasting in Smart Grids: A Review

Geoscience Solutions for Sustainable Offshore Wind Development

Modeling Wind Speed Based on Fractional Ornstein-Uhlenbeck Process

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