When designing “multi-MW arrays” of Wave Energy Converters (WECs), having a low number of converters with high individual power ratings can be beneficial as the Operation and Maintenance (O&M) costs may be reduced. However, having converters of small dimensions or small power ratings could also be beneficial, as suggested by previous works, due to a reduction in material costs as compared to power production, and the use of small, inexpensive vessels. In this work, a case study investigating the optimum size of WEC for a 20 MW array is performed. Analysis is carried out based on the CorPower Ocean technology. In this case study, firstly a Levelized Cost of Energy (LCOE) model is created. This model incorporates the latest Capital Expenditure (CAPEX) estimates for CorPower Ocean’s 250 kW prototype. Using this techno-economic model, several sizes/ratings of WEC are tested for use in a 20 MW array. Operational Expenditure (OPEX) is calculated using two different calculation approaches in order to check its influence on final indicators. OPEX is firstly calculated as a percentage of CAPEX, as shown in previous works, and secondly using a failure-repair model, taking into account individual failures of WECs in the array. Size/rating analysis is carried out for several European locations in order to establish any dependence between site location and optimal WEC size/rating. Several metrics for techno-economic assessment of marine energy converters, other than LCOE, are compared in this work. A comparison of several devices with each these metrics is performed within this study.

This paper examines technical and non-technical factors that are critical to the viability of commercialization of wave energy converters in off-grid luxury resorts and small utilities. Critical factors are found by investigating Levelized Cost of Energy, and using the tools PESTEL and Porter’s five competitive forces. Identified factors are then applied on three business cases to investigate their impact on viability. The results show that one of the main challenges facing off-grid commercialization is the few wave energy converter units installed per location, negating the economy of scale that large wave energy farms count on to achieve competitive cost levels. In addition, factors like current cost of energy, available wave resources, distance from shore, infrastructure, supply chain logistics, and electricity demand are found to be deciding factors for viability. Despite these challenges, it is found that there are potentially viable off-grid business cases for commercialization of wave energy converters.

Critical to evaluating the economic viability of a wave energy project is: (1) a robust estimate of the electricity production throughout the project lifetime and (2) an understanding of the uncertainty associated with said estimate. Standardization efforts have established mean annual energy production (MAEP) as the metric for quantification of wave energy converter (WEC) electricity production and the performance matrix approach as the appropriate method for calculation. General acceptance of a method for calculating the MAEP uncertainty has not yet been achieved. Several authors have proposed methods based on the standard engineering approach to error propagation, however, a lack of available WEC deployment data has restricted testing of these methods. In this work the magnitude and sensitivity of MAEP uncertainty is investigated. The analysis is driven by data from simulated deployments of 2 WECs of different operating principle at 4 different locations. A Monte Carlo simulation approach is proposed for calculating the variability of MAEP estimates and is used to explore the sensitivity of the calculation. The uncertainty of MAEP ranged from 2%–20% of the mean value. Of the contributing uncertainties studied, the variability in the wave climate was found responsible for most of the uncertainty in MAEP. Uncertainty in MAEP differs considerably between WEC types and between deployment locations and is sensitive to the length of the input data-sets. This implies that if a certain maximum level of uncertainty in MAEP is targeted, the minimum required lengths of the input data-sets will be different for every WEC-location combination.

The key market drivers for marine energy are to reduce carbon emissions, and improve the security and sustainability of supply. There are other technologies that also meet these requirements, and therefore the marine energy market is dependent on the technology being cost effective, and competitive. The potential UK wave and tidal stream energy market is assessed using ETI’s energy systems modelling environment (ESME) which uses a multi-vector approach including energy generation, demand, heat, transport, and infrastructure. This is used to identify scenarios where wave and tidal energy form part of the least-cost energy system for the UK by 2050, and will assess what Levelised Cost of Energy (LCOE) reductions are required to improve the commercialization rate. The results indicate that an installed capacity of 4.9 GW of wave and 2.5 GW of tidal stream could be deployed by 2050 if the LCOE is within 4.5 and 7 p/kWh for each respective technology. If there is a step reduction to the LCOE of wave energy, however, a similar capacity of 5 GW could be deployed by 2050 at a LCOE of 11 p/kWh.

The majority of WEC (wave energy converter) projects are expensive and pose a large risk to a developer. Currently no developers have been successful in commercialising a WEC. So far, many wave energy feasibility studies have only considered the LCOE (levelised cost of electricity), assessing investment in marine energy technologies from a purely financial point of view. No previous studies have, however, explicitly accounted for development risk as well as the LCOE to determine the feasibility of a project. This paper proposes a new methodology that can be used to account for both risk and the LCOE to give a clearer picture of the feasibility of a WEC development. By combining the LCOE and risk score for a particular development, the “value for risk” can be calculated, presented here as the “RR ratio” (“Risk/Reward ratio”). A number of case studies were chosen to test the model, investigating the RR ratio for a number of different WEC technologies and ranking them to suggest an optimal development path for the industry. Results showed that projects that combine many innovative technologies provide the best “value for risk”. These devices overall had the highest risk, suggesting that multiple developers are likely required to collaborate in order to reduce the risk down to acceptable levels for each.