Current developments in wave energy conversion have focused on locations where the wave energy resource is the highest; using large devices to generate hundreds of kilowatts of power. However, it is possible to generate power from low power waves using smaller wave energy devices. These lower rated wave energy converters can form arrays to supply power to remote coastal or island communities which are off-grid. This study introduces wave-to-wire modelling of wave energy arrays for off-grid systems using low power permanent-magnet linear generators (PMLGs). Offshore energy storage at the DC link is added to keep the voltage constant along with a current controller for the inverter in order to supply constant low harmonic power to the residential load connected off-grid. Simulation results produced in MATLAB/Simulink environment show that the wave energy array can generate power independently from the residential side by keeping the system stable using offshore storage. In addition, two different types of controllers for wave energy devices that use PMLGs are compared based on the power captured from the waves.

This study presents an investigation to assess the motions experienced by a floating hybrid spar-tension leg platform structure when supporting a wind-monitoring lattice tower in deep water conditions of the Central Mediterranean. The numerical study is based on the software package ANSYS^® AQWA™. The structure supports a 30 m wind-monitoring tower as well as a LiDAR system. A parametric analysis was carried out for different geometrical and met-ocean conditions, and the simulations were restricted to regular (single frequency) wave and constant wind speed conditions only. The Morison formulation was used to resolve the hydrodynamic loads in a time domain. The study shows how the natural periods of the floating wind-monitoring mast structure decrease with increasing buoyancy-to-weight ratios. From the time-series simulations, it was evident that slender spars experience smaller displacements. This is a favourable result as it results in more reliable wind measurements taken by the cup-type anemometers. Finally, a sensitivity analysis was carried out to examine the variations of surge motion predictions resulting from deviations in the hydrodynamic coefficients. It was observed that the platform surge motion is more sensitive to deviations in the added mass coefficient than the drag coefficient.

For seismic assessment of wind turbines in seismically active areas, International Standards and Guidelines allow combining two separate analyses under environmental and earthquake loads, respectively. Time-domain or response-spectrum-based analyses are prescribed to compute the separate earthquake response. While some work has already been done to estimate the accuracy of uncoupled analyses for land-based wind turbines, to date no similar studies have been carried out for offshore ones. This paper investigates two different implementations of uncoupled analyses for seismic assessment of offshore wind turbines, considering the case study of a 5 MW wind turbine mounted on a tripod in intermediate waters.

Many islands have high electricity prices due to the reliance on imported diesel. However, hybrid energy systems (HES) which combine renewable generation with backup generators and energy storage are becoming cost competitive. Diesel usually provides about 10% of the demand because most renewables are non-dispatchable and thus the complete decarbonisation requires massively oversized renewable generation and storage. By including renewables with different resource profiles and demand side management (DSM), the diesel consumption could be decreased without increasing storage and renewable generation capacities. Here a framework for the design and optimisation of HES using wind, wave and solar generation and DSM is introduced. For the Mediterranean it is shown that wave energy is not competitive but that DSM reduces the emissions and costs by 21 and 8%. In the North Sea, DSM has lower benefits because waves act as an energy store for the wind. Thus, the combination of wave energy converters (WECs) and wind turbines significantly reduces the need for backup generation and energy storage which leads to large reductions in costs (up to 40%) and emissions (up to 60%). DSM and WECs can both simultaneously reduce the cost and emissions of HES but need to be designed for the particular circumstances.

This study illustrates the aerodynamic and mechatronic design of a 1/75 scaled model of the DTU 10 MW wind turbine to perform wind tunnel tests in floating offshore configuration. Due to the strong discrepancy of the Reynolds number between full and model scale (up to 150), a dedicated low-Reynolds airfoil (SD7032) was chosen for the aerodynamic design of the blades, and the final shape was defined based on a dedicated optimisation algorithm which had as target the matching of the scaled thrust force and the first flap-wise bending frequency, as it is thoroughly explained in the study. Furthermore, the mechatronic design is reported in terms of the design choices adopted to get the best target-oriented functionalities to the model (i.e. individual pitch control, bandwidth) and to reduce as much as possible the weights, greatly affecting the aero-elastic scaling. The results gathered during experimental campaigns at Politecnico di Milano wind tunnel, are reported confirming the validity of the design and manufacturing choices.

Wave parameter relationships have long been determined using methods that give non-standard and often inaccurate results. With increased commercial activity in the marine sector, the importance of accurate wave parameter relationship determination has become increasingly apparent. The outputs of many numerical models and buoy datasets do not include all requisite wave parameters, and a typical approach is to use a constant conversion factor or relationship based on defined spectra such as the Bretschneider or the joint North Sea wave observation project (JONSWAP) spectrum to determine these parameters. Given that relationships between wave parameters vary significantly over both hourly and seasonal and annual timescales, the currently employed methods are lacking, as subtleties are missed by the simpler approach. This paper addresses the determination of wave parameter relationships using a machine learning (ML)-based model, identifying and selecting the optimal method for the conversion of wave parameters (T _e, T ₀₁) in coastal Irish Waters. This approach is then validated at two sites on the West coast of Ireland. The aim is to highlight the utility of ML in approximating the relationship between wave parameters; using both buoy and modelled data, and mapping the predicted outcomes for a wave energy converter based on a variety of ML and measure correlate predict approaches.

This study investigates the use of the electrochemical impedance spectroscopy (EIS) technique as an alternative to the DC pulses technique for estimating the power capability decrease of lithium-ion batteries during calendar ageing. On the basis of results obtained from calendar ageing tests performed at different conditions during 1–2 years, a generalised model that estimates the battery power capability decrease as function of the resistance Rs increase (obtained from EIS) was proposed and successfully verified.

It is expected that large farms of wave energy converters (WECs) will be installed and as part of the consenting process it will be necessary to quantify their impact on the local environment. The objective of this study is to assess the impact a WEC farm has on the incoming wave field through the use of a novel methodology. This methodology assesses the changes of the significant wave height surrounding a flap-type WEC farm with a special focus on the lee of the farm. A time-dependent mild-slope equation model is employed to solve the propagation of surface waves and their interaction with the devices. The model represents the devices as obstacle cells with attributed absorption coefficients tuned against near-fields obtained from a boundary element method (BEM) solver. The wake effect of the farm is determined by using a step-by-step approach starting first with an assessment of one device and progressively incrementing to a larger number of flaps. The effect of incident sea states, device separations and water depth changes on the wake effect of the farm is also investigated. This work shows the potential of a WEC farm to reduce significant wave heights on the leeside.

The pollution coming from the use of gas turbines (GTs) in oil and gas (O&G) platforms requires greener solutions. One option is to use wind turbines (WTs) to supply the water injection systems (WISs) and explore smart energy management to make the O&G sector more sustainable. The effect of WT integration, and their the coordination with the local GTs, needs to be carefully analysed since the intermittency of the wind can jeopardise the stability of the offshore grid. The existence of flexible loads (FLEX) interfaced by variable speed drives (VSD), such as WISs, can help overcome the challenges related to wind intermittency and rotor angle stability: suitable control of such non-essential loads can be implemented to reduce the effect of wind power fluctuations, balance power generation and consumption and contribute to the optimal efficiency of the GT adjusting its loading conditions. This work investigates the system dynamics under short-term wind-induced power fluctuations, analysing the evolution of electrical variables, such as active power and generator rotor speed, to evaluate the possible arise of low-frequency oscillations. The impact of an adequate WIS load control to increase the system damping is also explored and an algorithm for system damping estimation is proposed.

Wave energy is one of the most attractive forms of renewable energy. The reasons include its promising availability, predictability, persistence, and power density. This study focuses on all linear generator designs and technologies which have been used so far in direct-drive wave energy converters (DD-WECs). Currently, linear permanent magnet generators (LPMG) have been proposed as the most advantageous generator system developed for DD-WECs. After a brief description of linear generator based wave energy converters, all proposed state-of-the-art of LPMG topologies available in the literature are discussed and compared in terms of flux path, core type, location of PMs, and etc. In addition, other linear generator technologies such as linear switched reluctance and linear superconducting generators, as an alternative to LPMGs, are reviewed. Finally, based on the surveyed quantitative comparisons performed in previous works, eight major concepts are evaluated in terms of economic and operational aspects.

Condition monitoring (CM) is considered an effective method to improve the reliability of wind turbines (WTs) and implement cost-effective maintenance. This study presents a single hidden-layer feedforward neural network (SLFN), trained using an extreme learning machine (ELM) algorithm, for CM of WTs. Gradient-based algorithms are commonly used to train SLFNs; however, these algorithms are slow and may become trapped in local optima. The use of an ELM algorithm can dramatically reduce learning time and overcome issues associated with local optima. In this study, the ELM model is optimised using a genetic algorithm. The residual signal obtained by comparing the model and actual output is analysed using the Mahalanobis distance (MD) measure due to its ability to capture correlations among multiple variables. An accumulated MD value, obtained from a range of components, is used to evaluate the health of a gearbox, one of the critical subsystems of a WT. Models have been identified from supervisory control and data acquisition (SCADA) data obtained from a working wind farm. The results show that the proposed training method is considerably faster than traditional techniques, and the proposed method can efficiently identify faults and the health condition of the gearbox in WTs.

The proton exchange membrane fuel cell (PEMFC) can be operated at different points such as the maximum power point (MPP) to extract maximum power and the maximum efficiency point (MEP) to operate at maximum efficiency. However, the different tracking techniques influence the cost of electricity (COE) of the fuel cell generation system. In this paper, the economic analysis of the PEMFC with the MPP tracking (MPPT) and MEP tracking (MEPT) techniques using the HOMER energy system analysis software is presented. A detailed comparison of the economic impact of the tracking techniques for ten load configurations of a standalone fuel cell generation system, which includes combined heat and power (CHP) loads is presented and discussed. Finally, based on economic considerations, a procedure to select a suitable tracking technique for particular requirements of the standalone PEMFC application is proposed. It is found that in the case of CHP configuration, the MPPT technique is the preferred technique to achieve low COE.

This study investigates the effect of wind energy penetration on the frequency response of a multi-machine power network considering different time constants of a low-pass filter (LPF) in the DC chopper of an energy capacitor system (ECS). The power network is made up of wind farm composed of fixed speed induction generator interconnected to a steam and hydro synchronous power plants. Heavy and light loads were connected in the system. The ECS is connected at the terminal of the wind turbine. The LPF time constant parameter was varied for different cases considering the same wind speed for the wind turbine. Scenarios with and without considering the synergy of a series dynamic braking resistor (SDBR) connected to the stator of the wind turbine and the ECS were investigated. The results obtained were compared to a case where no frequency control was employed in the power network. Simulations were carried out in power system computer aided design and electromagnetic transient including DC. The results show that a higher time constant of the LPF effectively damp the oscillations of the grid variables and restores quickly the system during network disturbance. The SDBR was used to further enhance the performance of the ECS.

Integrating wind and solar energy resources poses intermittency to power systems, which faces independent system operators with new technical and economic challenges. This study proposes a novel model to integrate the uncertainties of wind power on the supply side and roof-top solar photovoltaic (PV) on the demand side. To cope with their uncertainties, a demand response (DR) aggregator is proposed, which is enabled to participate in reserve markets. To this end, a new DR model is developed considering both customers’ options to reduce and increase load through the DR aggregator. As such, besides improving the existing DR models (load shifting and curtailment), two DR programmes, i.e. load growth and load recovery, are mathematically modelled. Numerical studies indicate the effectiveness of the proposed model to reduce the total operation cost of the system and facilitate the integration of wind power and roof-top PV.

This study introduces a probabilistic optimisation model for allocation of renewable distributed generations (DGs) in radial distribution networks. The methodology is based on a probabilistic generation – load model that combines all possible operating conditions of the wind-based DG units as well as load levels with their probabilities. A multiobjective performance index is extracted that is formulated as a combination of two indices, namely energy losses reduction and voltage improvement. Besides, a probabilistic AC optimal power flow is used to determine the optimal allocation of wind DG and maximise the multiobjective performance index. Two alternative control approaches of the future smart grids, i.e. area based under load tap changer control and adaptive power factor control, are assessed to maximise potential benefits and expand the penetration level of DGs. At first, this problem is formulated as a mixed-integer non-linear programming (MINLP) which leads to a computationally NP-hard problem. Accordingly, the obtained MINLP problem is relaxed and reformulated in the form of a well-suited second-order cone programming problem which is computationally efficient scheme to be solved. The implementation of the proposed framework on 4-bus and IEEE 33-bus radial distribution systems shows the performance of the proposed optimisation mechanism.