# Mdp1 _sciences_
###### Abstract
\begin{table}
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**Authors** & **Year** & **Title** & **Main Objective** & **Main Contribution** & **Subject Classification** \\ \hline [PERSON] [20] & 2020 & A new strategy for short-term ramp rate control in wind farms & Develop a no-storage proposal for negative ramp reduction of wind farms & Analytical framework assessing compliance, considering a finite forecast error & Negative ramp reduction by wind farm self-regulation \\ \hline [PERSON] et al. [21] & 2020 & Reducing the impact of wind farms on the electric power system by the use of energy storage & Assess the cycling cost of coal-fired plants due to wind power intermittency & Evaluation of different alternatives with varying degree of storage & Conventional plant cycling/storage \\ \hline [PERSON] et al. [22] & 2019 & Assessing the Effect of Wind Farm Layout on Energy Storage Requirement for Power Fluctuation Mitigation & Simultaneous optimization of wind farm output and battery size & Development and use of stochastic wind farm model to devise optimal wind farm LO & Turbulence/WF modeling/storage \\ \hline [PERSON] et al. [2019] & 2019 & Coordinated Control Strategies of PMSG-Based Wind Turbine for Smoothing Power Fluctuations & Smooth power output by de-loading, pliching, and DC-link & Evaluation of the benefit of a hierarchical use of the three strategies & Turbine control \\ \hline [PERSON] et al. [8] & 2019 & Dynamic modeling and design of a hybrid CASE \& WT system for wind power fluctuation reduction & Model the capability of a CAES system to reduce short-term fluctuations & Thermodynamic analysis of a wind turbine operating with a CAESS & Storage modeling \\ \hline [PERSON] et al. [5] & 2019 & Output power smoothing control approaches for wind and photovoltaic generation systems A review & Review techniques for reduction of power output fluctuations wind and PV & Review techniques with and without storage systems & Storage technologies (review) \\ \hline [PERSON] et al. [12] & 2019 & Optimizing wind farm setting to reduce power system impacts of wind variability & Bi-objective optimization to reduce residual demand and wind power variability & Algorithms for sitting of new wind farms & Fluctuation mitigation through wind farm sitting \\ \hline [PERSON] et al. [2018] & 2018 & Study on the ramp fluctuation suppression control wind power generation output using optimization method & Bi-objective optimization to reduce wind power variability and storage size & New grid control method & Grid control with storage and power forecast \\ \hline [PERSON] et al. [10] & 2017 & Overview of wind intermittency: Impacts, measurements, and mitigation solutions & In-depth review of wind power intermittency and mitigating measures & Quantitative measures of intermittency, grid integration costs etc. & Storage technologies (review) \\ \hline [PERSON] et al. [24] & 2016 & Ramp Event Forecast Based Wind Power Ramp Control With Energy Storage System & Devise a wind power ramp control method using storage & Optimal control method for ramp control with storage & Ramp control/storage \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overview of the recent literature on the study and mitigation of wind power ramps. Current methods for forecasting wind speed or wind power can be grouped into the following general approaches [30]: (1) Physical methods with parametric models of the atmosphere [31], (2) statistical methods such times series models [32; 33] and artificial neural networks [34], (3) machine-learning approaches with either supervised or interactive learning [35], and (4) hybrid methods combining different techniques in order to perform short-term and medium-term predictions [36]. Among the different approaches, the Kalman filter (KF) method stands out in its ability to provide accurate wind speed forecasts [37]. For instance, [PERSON] et al.