Application of technology development index and principal component analysis and cluster methods to ocean renewable energy facility siting

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To assist in siting of offshore renewable energy facilities (wind, wave, and in-stream tidal, and ocean current), a marine spatial planning-based approach is proposed. The first level (Tier #1) screening determines the potential energy resource to be exploited and then identifies areas that are prohibited from siting because there is a direct, irreconcilable conflict, as determined by a stakeholder process and vetted by regulators. Areas that remain after these exclusions are implemented are candidates for facility siting. The next step involves considering technical (engineering and economic) attributes of the proposed energy development that further restricts the area under consideration. Finally, Tier #2 screening (not addressed here) evaluates other use conflicts such as recreational and commercial fishing areas, marine mammal feeding and breeding grounds and transit paths, bird migratory paths, feeding, and nesting areas, and similar issues that must be considered in facility siting. To facilitate the application of technology constraints on siting, two methods are proposed, a Technology Development Index (TDI) and a Principal Components - Cluster Analysis (PCCA). The TDI method, developed by the authors and presented in this paper, is the ratio of the Technical Challenge Index (TCI) to the Power Production Potential (PPP) of the energy extraction device. TCI is a measure of how difficult it is to site the device at a given location plus a measure of the distance to the closest electrical grid connection point. The PPP is an estimate of the annual power production of one of the devices. The site with the lowest TDI represents the optimum location. In practice, the study area is gridded and the TDI (TCI and PPP) is calculated for each grid. The method explicitly accounts for the spatial variability of all input data. Simulations can be performed either deterministically or stochastically, using a Monte Carlo method, so that uncertainties in the underlying input data are reflected in the estimated values of the TDI. The later approach allows detailed assessment of the sensitivity of the estimates to the input data and formulations of the TCI and PPP. The results are presented in the form of contours of TDI. The method can be applied to any offshore renewable energy type or extraction system once the technical attributes are specified. The PCCA approach uses several spatially varying variables that describe the key attributes of the siting decision (e.g., water depth, power production potential, distance to shore, and seabed conditions). The principal components are first determined from the gridded data and then clusters are identified. Finally, the clusters are mapped to the study area. The attributes and spatial distribution of clusters provide insight into the optimum locations for development. The two methods were employed in identifying potential areas for siting of a wind farm in coastal waters of Rhode Island, assuming lattice jacket support structures for the wind turbines. Both methods give consistent results and show locations where the ratio of technical challenge to power production is minimized.

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Marine Technology Society Journal