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].

Although wave energy prototypes have been proposed for more than 100 years, they have still not reached full commercialisation. The reasons for this are varied, but include the diversity of device operating principles, the variety of onshore/nearshore/offshore deployment possibilities, the diversity of the wave climate at various potential wave energy sites, and the consequent lack of convergence in technology and consensus. This distributed effort has, in turn, lead to a slow rate of progression up the learning curve, with a significant number of wave energy company liquidations and technical setbacks dampening investor confidence. Although a number of reviews on wave energy technology are already in the published literature, such a dynamic environment merits an up‐to‐date analysis and this review examines the wave energy landscape from a technological, research and commercial perspective.

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.

Mathematical analysis is an essential tool for the successful development and operation of wave energy converters (WECs). Mathematical models of moorings systems are therefore a requisite in the overall techno-economic design and operation of floating WECs. Mooring models (MMs) can be applied to a range of areas, such as WEC simulation, performance evaluation and optimisation, control design and implementation, extreme load calculation, mooring line fatigue life evaluation, mooring design, and array layout optimisation. The mathematical modelling of mooring systems is a venture from physics to numerics, and as such, there are a broad range of details to consider when applying MMs to WEC analysis. A large body of work exists on MMs, developed within other related ocean engineering fields, due to the common requirement of mooring floating bodies, such as vessels and offshore oil and gas platforms. This paper reviews the mathematical modelling of the mooring systems for WECs, detailing the relevant material developed in other offshore industries and presenting the published usage of MMs for WEC analysis.

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.