Fused portfolio optimization for harnessing marine renewable energy resources

Offshore wind and marine hydrokinetic energy are underutilized energy resources. Efficiently exploiting these energy resources requires the identification of optimal deployment locations and optimal designs for offshore energy harvesting devices. These devices have the potential to be deployed in tandem such that the suite of devices consistently saturates a given power transmission system. To better understand the economic viability of harvesting marine renewable energy, a portfolio optimization is presented here. Portfolio optimization frameworks help to identify optimal deployment maps for energy-harvesting devices in a given domain and unify solutions of resource, technical performance, transmission, and cost model sub-problems into a unique and comprehensive tool. These frameworks select the energy-harvesting device designs in advance. This work proposes a portfolio optimization framework combined with optimal device design, sizing, and selection to enable a more realistic energy depiction that is beneficial to stakeholders. By maximizing power sent back to shore subject to a constraint on the levelized cost of energy, the algorithm creates an optimal mapping of devices that produces the maximum transmittable power and stabilizes portfolio variability in a cost-effective manner. Any reliably modeled offshore energy-harvesting device can be used within this framework. In this work, wind turbines and marine hydrokinetic kites are selected as a case study considering they are leading technologies for harvesting their respective energies. Results from this case study demonstrate optimal portfolios of devices for a location off the coast of North Carolina and show the utility of fusing device design optimization with the portfolio optimization. • Reliable, transparent marine hydrokinetic energy-harvesting kite design model developed. • Novel fusion of energy-harvesting device optimization and portfolio optimization. • Fusion can result in lower costs per unit energy across the entire deployment.