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Revenue maximisation and storage utilisation for the Ocean Grazer wave energy converter: a sensitivity analysis
Author(s): Jose de Jesus Barradas-Berglind, Tom Dijkstra, Yanji Wei, Marijn van Rooij, Harmen Meijer, Wout A. Prins, Antonis I. Vakis, Bayu Jayawardhana
Source: IET Renewable Power Generation, Volume 12, Issue 11, August 2018, p. 1241 - 1248
Type: Article
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This study presents a revenue maximisation strategy for market integration of a novel wave energy converter (WEC), part of the Ocean Grazer platform. In particular, the authors evaluate and validate the aforementioned revenue maximisation model predictive control (MPC) strategy through extensive simulations and by checking the underlying assumptions of the strategy implementation. Accordingly, an annual simulation of the MPC strategy is shown, which illustrates seasonality effects; furthermore, a benchmark against a heuristic strategy is presented, followed by analyses of the parameter sensitivity and the assumptions on the control loop information that the MPC receives. These efforts shed some light on the impact of variations of the considered parameters and variables on the total revenue and provide insights to optimally scale the WEC. Lastly, the challenges associated with the deployment of such a strategy are addressed, followed by concluding remarks.

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Non-linear LS-SVM with RBF-kernel-based approach for AGC of multi-area energy systems
Author(s): Gulshan Sharma, Akhilesh Panwar, Ibraheem Nasiruddin, Ramesh C. Bansal
Source: IET Generation, Transmission & Distribution, Volume 12, Issue 14, August 2018, p. 3510 - 3517
Type: Article
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Support vector machine (SVM) is very efficacious in pattern recognition and function estimation problems. However, its application to design efficient automatic generation control (AGC) scheme has not been investigated till date by the researchers and power engineers worldwide. Hence, in this study an early attempt has been made to present a combined design of optimal control and optimisation problem of least-squares SVM (LS-SVM)-based AGC of multi-area energy systems. The radial basis function (RBF) kernel is used to train the LS-SVM. To validate the efficacy of the approach, the proposed control method is implemented on various 2-area system models such as non-reheat thermal, reheat thermal and hydro-thermal energy systems. The trained LS-SVM RBF-kernel-based AGC is realised with 1% load perturbation in one of the control area and the obtained results are compared with multi-layer perceptron artificial neural network and the conventional integral based controller in order to show the supremacy of the proposed control design. Furthermore, the performance of the AGC design is examined considering the system non-linearities such as governor dead-band and generation rate constraint.

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