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Abstract The kinetics of methane hydrate decomposition was studied using a semibatch stirred-tank reactor. The decomposition was accomplished by reducing the pressure on a hydrate slurry in water to a value below the three-phase equilibrium pressure at the reactor temperature. The data were obtained at temperatures from 274 to 283 K and pressures from 0.17 to 6.97 MPa. The stirring rates were high enough to eliminate mass-transfer effects. Analysis of the data indicated that the decomposition rate was proportional to the particle surface area and to the difference in the fugacity of methane at the equilibrium pressure and the decomposition pressure. The proportionality constant showed an Arrhenius temperature dependence. An estimate of the hydrate particle diameters in the experiments permitted the development of an intrinsic model for the kinetics of hydrate decomposition.

Abstract The kinetics of methane hydrate decomposition was studied using a semibatch stirred-tank reactor. The decomposition was accomplished by reducing the pressure on a hydrate slurry in water to a value below the three-phase equilibrium pressure at the reactor temperature. The data were obtained at temperatures from 274 to 283 K and pressures from 0.17 to 6.97 MPa. The stirring rates were high enough to eliminate mass-transfer effects. Analysis of the data indicated that the decomposition rate was proportional to the particle surface area and to the difference in the fugacity of methane at the equilibrium pressure and the decomposition pressure. The proportionality constant showed an Arrhenius temperature dependence. An estimate of the hydrate particle diameters in the experiments permitted the development of an intrinsic model for the kinetics of hydrate decomposition.

ABSTRACTWind power forecasts are useful tools for power load balancing, energy trading and wind farm operations. Long range monthly‐to‐seasonal forecasting allows the prediction of departures from average weather conditions beyond traditional weather forecast timescales, months in advance. However, it has not yet been demonstrated how these forecasts can be optimally transformed to wind power. The predictable part of a seasonal forecast is for longer monthly averages, not daily averages, but to use monthly averages misses information on variability. To investigate, here a model relating average weather conditions to average wind power output was built, based on the relationship between instantaneous wind speed and power production and incorporating fluctuations in air density due to temperature and wind speed variability. Observed monthly average power output from UK stations was used to validate the model and to investigate the optimal temporal resolution for the data used to drive the model. Multiple simulations of wind power were performed based on reanalysis data, making separate simulations based on monthly, daily and sub‐daily averages, using a distribution defined by the mean across the period to incorporate information on variability. Basing the simulation on monthly averages alone is sub‐optimal: using daily average winds gives the highest correlation against observations. No improvement over this is gained by using sub‐daily averages or including temperature variability. This signifies that to transform seasonal forecasts to wind power a compromise must be made between using the daily averages with debatable skill and the more predictable monthly averages, losing information on day‐to‐day variability.

ABSTRACTWind power forecasts are useful tools for power load balancing, energy trading and wind farm operations. Long range monthly‐to‐seasonal forecasting allows the prediction of departures from average weather conditions beyond traditional weather forecast timescales, months in advance. However, it has not yet been demonstrated how these forecasts can be optimally transformed to wind power. The predictable part of a seasonal forecast is for longer monthly averages, not daily averages, but to use monthly averages misses information on variability. To investigate, here a model relating average weather conditions to average wind power output was built, based on the relationship between instantaneous wind speed and power production and incorporating fluctuations in air density due to temperature and wind speed variability. Observed monthly average power output from UK stations was used to validate the model and to investigate the optimal temporal resolution for the data used to drive the model. Multiple simulations of wind power were performed based on reanalysis data, making separate simulations based on monthly, daily and sub‐daily averages, using a distribution defined by the mean across the period to incorporate information on variability. Basing the simulation on monthly averages alone is sub‐optimal: using daily average winds gives the highest correlation against observations. No improvement over this is gained by using sub‐daily averages or including temperature variability. This signifies that to transform seasonal forecasts to wind power a compromise must be made between using the daily averages with debatable skill and the more predictable monthly averages, losing information on day‐to‐day variability.

This paper assesses the applicability of the Frame of Reference (FoR) approach for the environmental monitoring of large-scale offshore Marine Renewable Energy (MRE) projects. The focus is on projects harvesting energy from winds, waves and currents. Environmental concerns induced by MRE projects are reported based on a classification scheme identifying stressors, receptors, effects and impacts. Although the potential effects of stressors on most receptors are identified, there are large knowledge gaps regarding the corresponding (positive and negative) impacts. In that context, the development of offshore MRE requires the implementation of fit-for-purpose monitoring activities aimed at environmental protection and knowledge development. Taking European legislation as an example, it is suggested to adopt standardized monitoring protocols for the enhanced usage and utility of environmental indicators. Towards this objective, the use of the FoR approach is advocated since it provides guidance for the definition and use of coherent set of environmental state indicators. After a description of this framework, various examples of applications are provided considering a virtual MRE project located in European waters. Finally, some conclusions and recommendations are provided for the successful implementation of the FoR approach and for future studies.

This paper assesses the applicability of the Frame of Reference (FoR) approach for the environmental monitoring of large-scale offshore Marine Renewable Energy (MRE) projects. The focus is on projects harvesting energy from winds, waves and currents. Environmental concerns induced by MRE projects are reported based on a classification scheme identifying stressors, receptors, effects and impacts. Although the potential effects of stressors on most receptors are identified, there are large knowledge gaps regarding the corresponding (positive and negative) impacts. In that context, the development of offshore MRE requires the implementation of fit-for-purpose monitoring activities aimed at environmental protection and knowledge development. Taking European legislation as an example, it is suggested to adopt standardized monitoring protocols for the enhanced usage and utility of environmental indicators. Towards this objective, the use of the FoR approach is advocated since it provides guidance for the definition and use of coherent set of environmental state indicators. After a description of this framework, various examples of applications are provided considering a virtual MRE project located in European waters. Finally, some conclusions and recommendations are provided for the successful implementation of the FoR approach and for future studies.

Abstract A 30 kW fixed-guide-vanes biradial turbine-generator set was designed by Instituto Superior Tecnico (IST) and manufactured by Kymaner, within the framework of the H2020 European project OPERA, for open sea testing at the Mutriku breakwater oscillating water column (OWC) power plant and at the IDOM MARMOK-A-5 OWC spar-buoy, deployed at BiMEP, Basque Country, Spain. The paper reports the design and construction of the prototype, and its dry testing at IST, in a variable-flow test rig, simulating the oscillating flow induced by irregular waves. Results show that the turbine exhibits a peak efficiency of 70% under steady-state conditions and an average efficiency of 56% for the control laws tested.