Search Results (5)

In the context of the efforts toward the technological development of wave energy converters, reliable estimations of the annual energy production that can be attained with a given device are fundamental for a sound evaluation of the related levelized cost of energy, which is crucial in the investment decision-making process. The lack of reliability in estimates of devices productivity can, in turn, be exacerbated by uncertainty in the available wave energy resource. The Climate Data Store of the Copernicus Climate Change Service delivers hindcast data and projections of the wave climate along the 20 m bathymetric contours of the whole European coastline, covering the periods 1976–2017 and 2040–2100. This work addresses the presence of long-term trends in wave power and the effect of these trends on wave energy exploitability and on the energy production of different wave energy converters to be installed along the Mediterranean, North African, and European Atlantic coastlines. The results show that the monthly variation in the wave energy resource will generally increase for most of the considered areas, up to double the current values in some locations. Wave energy converters will have to face more severe wave conditions, with relevant implications in terms of survivability. At the same time, the future annual energy production of the analyzed devices is expected to increase in many areas in the Mediterranean Basin (particularly in the nearest future scenario), as well as in the Baltic Sea and along the coasts of the UK and France and the north coasts of Spain. Full article

Wave energy provides a renewable and clear power for the future energy mix and fights against climate change. Currently, there are many different wave energy converters, but their costs of extracting wave energy are still much higher than other matured renewables. One of the best indicators of calculating the generating cost of wave energy is the ‘levelized cost of energy’ (LCOE), which is the combined capital expenditure (CAPEX), operational expenditure (OPEX), and decommissioning cost with the inclusion of the annual energy production, discount factor, and project’s lifespan. However, the results of the LCOE are in disagreement. Hence, it is important to explore the cost breakdown of wave energy by the wave energy converter (WEC), so for finding potential ways to decrease the cost, and finally compare it with other renewable energies. Different WECs have been installed in the same place; the Wave Dragon LCOE platform is the best one, with an energy conversion of EUR 316.90/MWh, followed by Pelamis with EUR 735.94/MWh and AquaBuOY with EUR 2967.85/MWh. Even when using different locations to test, the rank of the LCOE would remain unchanged with the different value. As the CAPEX and OPEX dramatically drop, the availability and capacity factors slowly increase, and the LCOE decreases from a maximum of USD 470/MWh to a minimum of USD 120/MWh. When the discount rate is down from 11% to 6%, the LCOE reduces from USD 160/MWh to USD 102/MWh. Under the ideal condition of the optimal combination of multiple factors, in theory, the LCOE can be less than USD 0.3/KWh. To better explore the LCOE for WECs, the detailed cost elements found in the CAPEX and OPEX have been examined for the scenarios of the undiscounted, half-discounted, and discounted cost models. When the AEP is discounted, the lowest LCOE is equal to USD 1.171/kWh in scene 2 when using a five-step investment, which is below the LCOE value of USD 1.211/kWh in scene 1 when using a two-step investment. Meanwhile, the highest LCOE amounts to USD 2.416/kWh using the five-step investment, whose value is below the LCOE of a two-step investment. When using a one-step investment in scene 3, the lowest LCOE is equal to USD 0.296/kWh, which accounts for 25% of the lowest value in the five-step investment. Meanwhile, the highest LCOE amounts to USD 0.616/kWh, which accounts for 24% of the highest value in the two-step investment. The results of the case study show that a one-step investment program in the half-discounted model is superior to the multi-step investment in the discounted model. This paper examines the viability of wave energy technologies, which is a critical factor for the LCOE of wave energy; furthermore, the form of investment in the wave energy project is also important when calculating the LCOE. Full article

A technique to analyse the economic viability of offshore farms composed of wave energy converters is proposed. Firstly, the inputs, whose value will be considered afterwards in the economic step, was calculated using geographic information software. Secondly, the energy produced by each wave converter was calculated. Then the economic factors were computed. Finally, the restriction that considers the depth of the region (bathymetry) was put together with the economic outputs, whose value depends on the floating Wave Energy Converter (WEC). The method proposed was applied to the Cantabric and Atlantic coasts in the north of Spain, a region with a good offshore wave energy resource. In addition, three representative WECs were studied: Pelamis, AquaBuoy and Wave Dragon; and five options for electric tariffs were analysed. Results show the Wave Energy Converter that has the best results regarding its LCOE (Levelized Cost of Energy), IRR (Internal Rate of Return) and NPV (Net Present Value), and which area is best for the development of a wave farm. Full article

The aim of this work is to develop a software to calculate the economic parameters so as to determine the feasibility of a floating offshore renewable farm in a selected location. The software can calculate the economic parameters of several types of offshore renewable energies, as follows: one renewable energy (floating offshore wind—WindFloat, tension leg platform (TLP), and spar; floating wave energy—Pelamis and AquaBuoy), hybrid offshore wind and wave systems (Wave Dragon and W2Power), and combined offshore wind and waves with different systems (independent arrays, peripherally distributed arrays, uniformly distributed arrays, and non-uniformly distributed arrays). The user can select several inputs, such as the location, configuration of the farm, type of floating offshore platform, type of power of the farm, life-cycle of the farm, electric tariff, capital cost, corporate tax, steel cost, percentage of financing, or interest and capacity of the shipyard. The case study is focused on the Galicia region (NW of Spain). The results indicate the economic feasibility of a farm of floating offshore renewable energy in a particular location in terms of its costs, levelized cost of energy (LCOE), internal rate of return (IRR), net present value (NPV), and discounted pay-back period. The tool allows for establishing conclusions about the dependence of the offshore wind resource parameters, the main distances (farm–shore, farm–shipyard, and farm–port), the parameters of the waves, and the bathymetry of the area selected. Full article

This paper develops a methodology to determine the economic feasibility of implementing offshore wave energy farms on the Portuguese continental coast. This methodology follows several phases: the geographic phase, the energy phase, the economic phase, and the restrictions phase. First, in the geographic phase, the height and the period of the waves, the bathymetry, the distance from the farm to the shore, from farm to shipyard, and from farm to port, are calculated. In the energy phase the energy produced by each wave energy converter is determined, and in the economic phase, the parameters calculated in the previous phases are used as input to find the economic parameters. Finally, in the restrictions phase, a limitation by the bathymetry will be added to the economic maps, whose value will be different depending on the floating offshore wave energy converter (WEC). In this study, three wave energy converters have been considered, Pelamis, AquaBuOY, and Wave Dragon, and several scenarios for electric tariffs have been taken into account. The results obtained indicate what the best WEC is for this study in terms of its levelized cost of energy (LCOE), internal rate of return (IRR), and net present value (NPV), and where the best area is to install wave energy farms. Full article