A comparison of efficiency-aware model-predictive control approaches for wave energy devices

2021 ◽  
Author(s):  
Nataliia Y. Sergiienko ◽  
Giorgio Bacelli ◽  
Ryan G. Coe ◽  
Benjamin S. Cazzolato
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Wave Energy ◽  
Energy Devices

scholarly journals Passive Model Predictive Control on a Two-Body Self-Referenced Point Absorber Wave Energy Converter

Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1731
Author(s):  
Dan Montoya ◽  
Elisabetta Tedeschi ◽  
Luca Castellini ◽  
Tiago Martins
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Wave Energy ◽  
Power Flow ◽  
Linear Constraints ◽  
Wave Energy Converter ◽  
Control Technique ◽  
Computational Time ◽  
Energy Converter ◽  
Point Absorber

Wave energy is nowadays one of the most promising renewable energy sources; however, wave energy technology has not reached the fully-commercial stage, yet. One key aspect to achieve this goal is to identify an effective control strategy for each selected Wave Energy Converter (WEC), in order to extract the maximum energy from the waves, while respecting the physical constraints of the device. Model Predictive Control (MPC) can inherently satisfy these requirements. Generally, MPC is formulated as a quadratic programming problem with linear constraints (e.g., on position, speed and Power Take-Off (PTO) force). Since, in the most general case, this control technique requires bidirectional power flow between the PTO system and the grid, it has similar characteristics as reactive control. This means that, under some operating conditions, the energy losses may be equivalent, or even larger, than the energy yielded. As many WECs are designed to only allow unidirectional power flow, it is necessary to set nonlinear constraints. This makes the optimization problem significantly more expensive in terms of computational time. This work proposes two MPC control strategies applied to a two-body point absorber that address this issue from two different perspectives: (a) adapting the MPC formulation to passive loading strategy; and (b) adapting linear constraints in the MPC in order to only allow an unidirectional power flow. The results show that the two alternative proposals have similar performance in terms of computational time compared to the regular MPC and obtain considerably more power than the linear passive control, thus proving to be a good option for unidirectional PTO systems.

scholarly journals Model Predictive Control for Wave Energy Converters: A Moving Window Blocking Approach

2020 ◽  
Vol 53 (2) ◽  
pp. 12815-12821
Author(s):  
Juan Guerrero-Fernández ◽  
Oscar J. González-Villarreal ◽  
John Anthony Rossiter ◽  
Bryn Jones
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Wave Energy ◽  
Moving Window ◽  
Wave Energy Converters ◽  
Energy Converters

scholarly journals Hardware-in-the-Loop Validation of Model Predictive Control of a Discrete Fluid Power Power Take-Off System for Wave Energy Converters

Energies ◽  
2019 ◽  
Vol 12 (19) ◽  
pp. 3668
Author(s):  
Anders H. Hansen ◽  
Magnus F. Asmussen ◽  
Michael M. Bech
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Wave Energy ◽  
Fluid Power ◽  
Wave Power ◽  
Reactive Control ◽  
Power Extraction ◽  
Wave Energy Converters ◽  
Extraction Algorithm ◽  
Energy Converters

Model predictive control based wave power extraction algorithms have been developed and found promising for wave energy converters. Although mostly proven by simulation studies, model predictive control based algorithms have shown to outperform classical wave power extraction algorithms such as linear damping and reactive control. Prediction models and objective functions have, however, often been simplified a lot by for example, excluding power take-off system losses. Furthermore, discrete fluid power forces systems has never been validated experimentally in published research. In this paper a model predictive control based wave power extraction algorithm is designed for a discrete fluid power power take-off system. The loss models included in the objective function are based on physical models of the losses associated with discrete force shifts and throttling. The developed wave power extraction algorithm directly includes the quantized force output and the losses models of the discrete fluid power system. The experimental validation of the wave power extraction algorithm developed in the paper shown an increase of 14.6% in yearly harvested energy when compared to a reactive control algorithm.

Performance of a Wave-Energy-Converter Array Operating Under Model-Predictive Control Based on a Convex Formulation

2018 ◽  
Author(s):  
Qian Zhong ◽  
Ronald W. Yeung
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Wave Energy ◽  
Reactive Power ◽  
Optimal Solution ◽  
Energy Output ◽  
Control Input ◽  
Energy Extraction ◽  
Power Performance ◽  
Point Absorbers

Economics decision drives the operation of ocean-wave energy converters (WEC) to be in a “farm mode”. Control strategy developed for a WEC array will be of high importance for improving the aggregate energy extraction efficiency of the whole system. Model-predictive control (MPC) has shown its strong potential in maximizing the energy output in devices with hard constraints on operation states and machinery inputs (See Ref. [1–3]). Computational demands for using MPC to control an array in real time can be prohibitive. In this paper, we formulate the MPC to control an array of heaving point absorbers, by recasting the optimization problem for energy extraction into a convex Quadratic Programming (QP) problem, the solution of which can be carried out very efficiently. Large slew rates are to be penalized, which can also guarantee the convexity of the QP and improve the computational efficiency for achieving the optimal solution. Constraints on both the states and the control input can be accommodated in this MPC method. Full hydro-dynamic interference effects among the WEC array components are taken into account using the theory developed in [4]. Demonstrative results of the application are presented for arrays of two, three, and four point absorbers operating at different incident-wave angles. Effects of the interacting waves on power performance of the array under the new MPC control are investigated, with simulations conducted in both regular and irregular seas. Heaving motions of individual devices at their optimal conditions are shown. Also presented is the reactive power required by the power takeoff (PTO) system of the array to achieve optimality. We are pleased to contribute this article in celebration of our collegiality with Professor Bernard Molin on the occasion of his honoring symposium.

scholarly journals Model Predictive Control Tuning by Inverse Matching for a Wave Energy Converter

Energies ◽  
2019 ◽  
Vol 12 (21) ◽  
pp. 4158 ◽  
Author(s):  
Hancheol Cho ◽  
Giorgio Bacelli ◽  
Ryan G. Coe
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Wave Energy ◽  
Wave Energy Converter ◽  
Energy Converter ◽  
Feedback Form ◽  
Linear Controller ◽  
Prediction Horizon ◽  
The Cost ◽  
Input And State Constraints

This paper investigates the application of a method to find the cost function or the weight matrices to be used in model predictive control (MPC) such that the MPC has the same performance as a predesigned linear controller in state-feedback form when constraints are not active. This is potentially useful when a successful linear controller already exists and it is necessary to incorporate the constraint-handling capabilities of MPC. This is the case for a wave energy converter (WEC), where the maximum power transfer law is well-understood. In addition to solutions based on numerical optimization, a simple analytical solution is also derived for cases with a short prediction horizon. These methods are applied for the control of an empirically-based WEC model. The results show that the MPC can be successfully tuned to follow an existing linear control law and to comply with both input and state constraints, such as actuator force and actuator stroke.

Economic Model Predictive Control of a Point Absorber Wave Energy Converter

2021 ◽  
Vol 12 (1) ◽  
pp. 578-586
Author(s):  
Yubin Jia ◽  
Ke Meng ◽  
Lu Dong ◽  
Tongming Liu ◽  
Changyin Sun ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Wave Energy ◽  
Economic Model ◽  
Wave Energy Converter ◽  
Energy Converter ◽  
Point Absorber ◽  
Economic Model Predictive Control

scholarly journals Towards Real-Time Reinforcement Learning Control of a Wave Energy Converter

2020 ◽  
Vol 8 (11) ◽  
pp. 845
Author(s):  
Enrico Anderlini ◽  
Salman Husain ◽  
Gordon G. Parker ◽  
Mohammad Abusara ◽  
Giles Thomas
Keyword(s):  
Reinforcement Learning ◽  
Model Predictive Control ◽  
System Dynamics ◽  
Real Time ◽  
Predictive Control ◽  
Wave Energy ◽  
Economies Of Scale ◽  
Computational Cost ◽  
Effective Control ◽  
Deployment Time

The levellised cost of energy of wave energy converters (WECs) is not competitive with fossil fuel-powered stations yet. To improve the feasibility of wave energy, it is necessary to develop effective control strategies that maximise energy absorption in mild sea states, whilst limiting motions in high waves. Due to their model-based nature, state-of-the-art control schemes struggle to deal with model uncertainties, adapt to changes in the system dynamics with time, and provide real-time centralised control for large arrays of WECs. Here, an alternative solution is introduced to address these challenges, applying deep reinforcement learning (DRL) to the control of WECs for the first time. A DRL agent is initialised from data collected in multiple sea states under linear model predictive control in a linear simulation environment. The agent outperforms model predictive control for high wave heights and periods, but suffers close to the resonant period of the WEC. The computational cost at deployment time of DRL is also much lower by diverting the computational effort from deployment time to training. This provides confidence in the application of DRL to large arrays of WECs, enabling economies of scale. Additionally, model-free reinforcement learning can autonomously adapt to changes in the system dynamics, enabling fault-tolerant control.

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