John Ringwood

Short-Term Wave Forecasting for Real-Time Control of Wave Energy Converters

Real-time control of wave energy converters requires knowledge of future incident wave elevation in order to approach optimal efficiency of wave energy extraction. We present an approach where the wave elevation is treated as a time series and it is predicted only from its past history. A comparison of a range of forecasting methodologies on real wave observations from two different locations shows how the relatively simple linear autoregressive model, which implicitly models the cyclical behavior of waves, can offer very accurate predictions of swell waves for up to two wave periods into the future.

Forecasting Electricity Demand on Short, Medium and Long Time Scales Using Neural Networks

This paper examines the application of artificial neural networks (ANNs) to the modelling and forecasting of electricity demand experienced by an electricity supplier. The data used in the application examples relates to the national electricity demand in the Republic of Ireland, generously supplied by the Electricity Supply Board (ESB). The paper focusses on three different time scales of interest to power boards: yearly (up to fifteen years in advance), weekly (up to three years in advance) and hourly (up to 24 h ahead). Electricity demand exhibits considerably different characteristics on these different time scales, both in terms of the underlying autoregressive processes and the causal inputs appropriate to each time scale. Where possible, the ANN-based models draw on the applications experience gained with linear modelling techniques and in one particular case, manual forecasting methods.

Energy-Maximizing Control of Wave-Energy Converters: The Development of Control System Technology to Optimize Their Operation

With the recent sharp increases in the price of oil, issues of security of supply, and pressure to honor greenhouse gas emission limits (e.g., the Kyoto protocol), much attention has turned to renewable energy sources to fulfill future increasing energy needs. Wind energy, now a mature technology, has had considerable proliferation, with other sources, such as biomass, solar, and tidal, enjoying somewhat less deployment. Waves provide previously untapped energy potential, and wave energy has been shown to have some favorable variability properties (a perennial issue with many renewables, especially wind), especially when combined with wind energy [1].

A Simple and Effective Real-Time Controller for Wave Energy Converters

A novel strategy for the real-time control of oscillating wave energy converters (WECs) is proposed. The controller tunes the oscillation of the system such that it is always in phase with the wave excitation force and the amplitude of the oscillation is within given constraints. Based on a nonstationary, harmonic approximation of the wave excitation force, the controller is easily tuned in real-time for performance and constraints handling, through one single parameter of direct physical meaning. The effectiveness of the proposed solution is assessed for a heaving system in one degree of freedom, in a variety of irregular (simulated and real) wave conditions. A performance close to reactive control and to model predictive control is achieved. Additional benefits in terms of simplicity and robustness are obtained.

A Study of the Prediction Requirements in Real-Time Control of Wave Energy Converters

It is widely acknowledged that real-time control of wave energy converters (WECs) can benefit from prediction of the excitation force. The prediction requirements (how far ahead into the future do we need to predict?) and the achievable predictions (how far ahead can we predict?) are quantified when unconstrained reactive control is implemented. The fundamental properties of the floating system that influence the length of the required forecasting horizon, as well as the achievable prediction, are characterized. The possibility of manipulating the control, based on prior knowledge of the wave spectral distribution, is also proposed for the reduction of the prediction requirements, such that they are within the range of predictability offered by simple stochastic predictors. The proposed methodology is validated on real wave data and heaving buoys with different geometries.

24-h electrical load data—a sequential or partitioned time series?

Variations in electrical load are, among other things, hour of the day dependent, introducing a dilemma for the forecaster: whether to partition the data and use a separate model for each hour of the day (the parallel approach), or use a single model (the sequential approach). This paper examines which approach is appropriate for forecasting hourly electrical load in Ireland. It is found that, with the exception of some hours of the day, the sequential approach is superior. The final solution however, uses a combination of linear sequential and parallel neural models in a multi-time scale formulation.

On the Influence of Weather Forecast Errors in Short-Term Load Forecasting Models

Weather information is an important factor in load forecasting models. Typically, load forecasting models are constructed and tested using actual weather readings. However, online operation of load forecasting models requires the use of weather forecasts, with associated weather forecast errors. These weather forecast errors inevitably lead to a degradation in model performance. This is an important factor in load forecasting but has not been widely examined in the literature. The main aim of this paper is to present a novel technique for minimizing the consequences of this degradation. In addition, a supplementary technique is proposed to model weather forecast errors to reflect current accuracy. The proposed technique utilizes a combination of forecasts from several load forecasting models (sub-models). The parameter estimation may thus be split into two parts: sub-model and combination parameter estimation. It is shown that the lowest PMSE corresponds to training the sub-models with actual weather but training the combiner with forecast weather.

A control system for a self-reacting point absorber wave energy converter subject to constraints

Abstract The problem of the maximization of the energy produced by a self reacting point absorber subject to motion restriction is addressed. The main objective is to design a control system suitable for real-time implementation. The method presented for the solution of the optimization problem is based on the approximation of the motion of the device and of the force exerted by the power take off unit by means of a linear combination of basis functions. The result is that the optimal control problem is reformulated as a non linear program where the properties of the cost function and of the constraint are affected by the choice of the basis functions. An example is described where the motion and the force are approximated using Fourier series; an optimization algorithm for the solution of the non linear program is also presented. The control system is implemented and simulated using a real sea profile measured by a waverider buoy.

Numerical Optimal Control of Wave Energy Converters

Energy maximizing control for wave energy converters (WECs) is a nonstandard optimal control problem. While the constrained optimal control problem for WECs has been addressed by model-predictive control strategies, such strategies need to employ cost function modifications due to convexity problems and the algorithms are computationally complex, making real-time implementation difficult. The recently developed family of direct transcription methods offer a promising alternative, since they are computationally efficient and a convex problem results. Moreover, constraints on both the device displacement and velocity, and power take off force, are easily incorporated. Both single-body and multibody device models can be used, as well as arrays of single-body or multibody devices.

Optimal Damping Profiles For a Heaving Buoy Wave Energy Converter

This paper discusses optimal damping profiles for a heaving buoy Wave Energy Converter (WEC) with a single degree of freedom. The goal is to examine how the device can be controlled to harvest maximum energy from incident waves. Both latching and declutching strategies are allowed via a general parametrization of the damping force. Ultimately, the research attempts to determine the best control strategy to apply considering the relative resonant frequency of the device and the monochromatic wave frequency set.