Annette R. Grilli

Protocol to Include Ecosystem Service Constraints in a Wind Farm Cost Model

AbstractThe Rhode Island Coastal Resources Management Council (CRMC) has been leading an Ocean Special Area Management Plan (SAMP) aimed at zoning the state’s coastal waters to accommodate offshore wind farm developments. In previous SAMP related work, the authors had considered the wind farm siting issue as an optimization problem between wind resources and technological constraints. In the present analysis, the additional constraints on wind farm siting of ecosystem services “cost”, in particular ecological and social services, are explored in an ecosystem-based management (EBM) conceptual framework. An ecological typology of the coastal area is developed on the basis of ecological variables, using spatial multivariate principal component and cluster analyses. Then, the sensitivity of the resulting ecological subregions to wind farm impact is assessed through the construction of ecological services impact indexes. A fishery service index is used to assess the fisheries services constraints. Ecosystem se...

Ocean wave energy harvesting buoy for sensors

Methodology and results are presented for the numerical simulations and experimental measurements on ocean energy harvesting systems that utilize anchored linear generators, driven by heaving surface buoys that convert ambient ocean wave energy into useful electrical power. The results demonstrate the feasibility of using ocean wave energy harvesting buoys and simple linear generators to provide sufficient electrical power for ocean sensor applications (1–10 W range). Experimental results for a small scale linear generator, directly driven in a test bed with realistic ocean wave spectra, demonstrate power in the 5–10 W range. Simulations for buoy/generator systems using imperfectly wave-compliant surface buoys and two different anchoring approaches show that the wave compliance of the buoy and the method of anchoring have a major effect on performance. These simulations for practical system designs show performance in the 1–4 W range.

Toward wind farm monitoring optimization: assessment of ecological zones from marine landscapes using machine learning algorithms

Abstract Within the perspective of siting wind farms offshore of Rhode Island, USA, the State and National Environmental Agencies had requested a local marine ecological assessment, which led to an ecological zoning of the area. In view of expanding this zoning outside its limit of the test area and filling gaps in ecological zones, an effort to model those ecological zones using marine landscape or abiotic features was carried out. This study tests the accuracy of selected machine learning algorithmic models, decision tree, and random forest, for relating marine landscapes features to ecological sub-regions. Both models show to be good predictive tools with accuracy after cross validation of the order of 5–3%. Key abiotic variables to provide an accurate model were investigated. The study demonstrates the importance of the distance to coast, the sediment characteristics (fraction of clay, median size of the sediments), the hydrodynamic features, in particular not only tidal current/drag force, but also wave drag force, and finally the oceanographic characteristics such as stratification and sea surface temperature to built a good predictive model. Those findings provide some insight on the pre-monitoring effort optimization.

Offshore wind farm siting using a genetic algorithm

This study uses a genetic algorithm to optimize a wind farm layout considering the engineering challenges and the ecosystem services as constraints to the turbine siting. Included is an analysis of how wake effects influence the power produced using a simple wake model called the WAsP model. The current study considers the location of the proposed Deep Water Wind Inc. Offshore Wind Farm Project, southeast of Block Island, Rhode Island. The proposed project consists of six, 6MW Siemens wind turbines located within the Rhode Island State waters that extend roughly 4.8 km off of the Block Island coast. The optimum solution produces turbine locations best conforming to areas of low technical, ecological, and social costs, while simultaneously distributing the turbines to minimize turbine wake interaction. Future model improvements will consist of more accurately describing wind conditions within the wind farm and incorporating turbine cable interconnection installation costs.

Experimental testing and model validation for ocean wave energy harvesting buoys

Methodology and results are presented for numerical simulations and field experiments using point absorption ocean wave energy harvesting buoy systems, using the heave motion of the buoys to produce useful electrical power. Two approaches, a direct-drive system and a resonant-drive system are analyzed. These systems are not designed for large scale grid power applications, but rather for relatively low-power ocean sensor and communications applications, with power requirements in the 1-10 W range. The field experiments provided useful data for model verification and validation purposes. Results showed that RMS values for armature displacement and armature velocity and mean harvested electrical power were generally within 10% between model simulations and experimental data.

Tsunami Detection by High Frequency Radar Beyond the Continental Shelf: II. Extension of Time Correlation Algorithm and Validation on Realistic Case Studies

In past work, tsunami detection algorithms (TDAs) have been proposed, and successfully applied to offline tsunami detection, based on analyzing tsunami currents inverted from high-frequency (HF) radar Doppler spectra. With this method, however, the detection of small and short-lived tsunami currents in the most distant radar ranges is challenging due to conflicting requirements on the Doppler spectra integration time and resolution. To circumvent this issue, in Part I of this work, we proposed an alternative TDA, referred to as time correlation (TC) TDA, that does not require inverting currents, but instead detects changes in patterns of correlations of radar signal time series measured in pairs of cells located along the main directions of tsunami propagation (predicted by geometric optics theory); such correlations can be maximized when one signal is time-shifted by the pre-computed long wave propagation time. We initially validated the TC-TDA based on numerical simulations of idealized tsunamis in a simplified geometry. Here, we further develop, extend, and apply the TC algorithm to more realistic tsunami case studies. These are performed in the area West of Vancouver Island, BC, where Ocean Networks Canada recently deployed a HF radar (in Tofino, BC), to detect tsunamis from far- and near-field sources, up to a 110 km range. Two case studies are considered, both simulated using long wave models (1) a far-field seismic, and (2) a near-field landslide, tsunami. Pending the availability of radar data, a radar signal simulator is parameterized for the Tofino HF radar characteristics, in particular its signal-to-noise ratio with range, and combined with the simulated tsunami currents to produce realistic time series of backscattered radar signal from a dense grid of cells. Numerical experiments show that the arrival of a tsunami causes a clear change in radar signal correlation patterns, even at the most distant ranges beyond the continental shelf, thus making an early tsunami detection possible with the TC-TDA. Based on these results, we discuss how the new algorithm could be combined with standard methods proposed earlier, based on a Doppler analysis, to develop a new tsunami detection system based on HF radar data, that could increase warning time. This will be the object of future work, which will be based on actual, rather than simulated, radar data.

Mapping the coastal risk for the next century, including sea level rise and changes in the coastline: application to Charlestown RI, USA

A source–pathway-receptor method is used to assess the risk of the coastal community of Charlestown, RI, USA, to the 100-year storm, including effects of sea level rise (SLR) and shoreline/dune erosion. The 100-year storm is simulated using a chain of stochastic and physics-based models combined with a scenario-based approach. Storm surge and wave spectral parameters, obtained from the U.S. Army Corps of Engineers’ North Atlantic Coast Comprehensive Study (NACCS), are used as boundary conditions for high-resolution wave simulations, performed in the coastal and inundation zones using the steady-state spectral wave model STWAVE. Selected scenarios are defined to assess the magnitude of the variability in predicted damage resulting from the uncertainty in SLR, erosion rate, and time at which the 100-year storm would occur. Erosion rates are based on empirical analyses of historic rates of shoreline change, SLR measurements, and coastal erosion theory. The risk is measured in terms of damage to individual houses, based on damage curves developed in the U.S. Army Corps of Engineers, NACCS study. In addition, remediation scenarios are explored, demonstrating that a combination of dune replenishment and an increase in the residential resilience by elevating structures can significantly diminish the risk to the coastal community.

Micrositing Optimization of the Block Island Wind Farm, RI, USA

The Rhode Island Ocean Special Area Management Plan (RIOSAMP) has been implemented in Rhode Island since 2008 to provide guidance to local regulators in the zoning of renewable energy, with a focus on the siting of offshore wind farms. The project culminated in the siting of the first North American offshore wind project, optimized using a spatial planning approach combining exclusionary and mitigating factors. The optimization of mitigating factors is based on a standard cost model approach and extended to include ecological and societal factors. This macro-siting optimization phase provided the framework to define a Renewable Energy Zone (REZ) for wind farm development and the present study seeks the siting optimization of the wind farm layout within this zone. The optimization considers the loss in power resulting from turbine wake interaction, a cable cost clustering algorithm, and the spatial variation of both foundation cost and the available wind resource within the REZ through a micrositing objective function. This initial objective function is extended to include ecological and social costs. The layout optimization is based on a Genetic Algorithm (GA) optimization scheme. The method is applied to the REZ area, demonstrating that a gain of approximately

Tsunami detection by high-frequency radar in British Columbia: performance assessment of the time-correlation algorithm for synthetic and real events

10 million over 20 years could be obtained if an “optimal layout” would be selected over the initial layout chosen by the developers.Copyright

Offshore Wind Resource Assessment in Rhode Island Waters

The authors recently proposed a new method for detecting tsunamis using high-frequency (HF) radar observations, referred to as “time-correlation algorithm” (TCA; Grilli et al. Pure Appl Geophys 173(12):3895–3934, 2016a, 174(1): 3003–3028, 2017). Unlike standard algorithms that detect surface current patterns, the TCA is based on analyzing space-time correlations of radar signal time series in pairs of radar cells, which does not require inverting radial surface currents. This was done by calculating a contrast function, which quantifies the change in pattern of the mean correlation between pairs of neighboring cells upon tsunami arrival, with respect to a reference correlation computed in the recent past. In earlier work, the TCA was successfully validated based on realistic numerical simulations of both the radar signal and tsunami wave trains. Here, this algorithm is adapted to apply to actual data from a HF radar installed in Tofino, BC, for three test cases: (1) a simulated far-field tsunami generated in the Semidi Subduction Zone in the Aleutian Arc; (2) a simulated near-field tsunami from a submarine mass failure on the continental slope off of Tofino; and (3) an event believed to be a meteotsunami, which occurred on October 14th, 2016, off of the Pacific West Coast and was measured by the radar. In the first two cases, the synthetic tsunami signal is superimposed onto the radar signal by way of a current memory term; in the third case, the tsunami signature is present within the radar data. In light of these test cases, we develop a detection methodology based on the TCA, using a correlation contrast function, and show that in all three cases the algorithm is able to trigger a timely early warning.