Economic, System, and Community-Based Optimization of Off-Grid Wave Energy Conversion

Abstract

The growing economic feasibility of renewable energy, caused in large part by the climate crisis, hasexpanded the number of locations in which humans can feasibly harness and use electricity. Wave energy, in particular, has the potential to power oceanographic measurements, remote communities, and other applications that may lack sufficient electricity due to limited grid access in ocean or coastal areas. However, as a relatively nascent renewable energy technology, less is known about the suitability and optimal characteristics of wave energy in off-grid applications. This thesis explores off-grid wave energy by focusing on its potential contributions to ocean observation, in which autonomous instrument packages can be powered by a wave energy converter (WEC) to collect information on metocean, biogeochemical, and ecological processes. Historically, constraints associated with battery-, vessel-, and cable-powered observations have limited the range, endurance, continuity, persistence and flexibility of these measurements, in turn restraining our understanding of marine ecosystems. This has driven decades of innovation to add “in-situ” power generation, such as wave energy, that enables new opportunities for ocean research. Aiding these efforts, this thesis presents optimization frameworks that simulate off-grid wave energy systems and identify costoptimal system design, as well as potential enhancements that may improve the suitability of wave energy for ocean observation and, consequently, off-grid applications in general. Chapter 1 describes the context of wave energy at a high level, including how the technology may beused at scales smaller than for megawatt-scale utility power, such as ocean observation. In Chapter 2, I present a techno-economic model for powering ocean observation that compares the system characteristics and cost drivers of four battery-backed sources of in-situ electrical power: solar photovoltaics, wind, wave, and diesel generation. Our model utilizes time-domain simulation and optimization to identify cost-optimal system characteristics (e.g., generation and battery storage capacities). Using this model, I evaluate the cost of in-situ generation sources to power a 200 W ocean observation system deployed for five years at three unique geographic locations. I find that cost-optimal system characteristics to meet this load depend on resource and location, such that generation capacities are lower for the wave and wind resources (< 1 kW at locations with strong resources, up to a maximum of 3.3 kW) and larger for the solar and diesel resources (> 3 kW, in certain cases exceeding 7.5 kW). Cost-optimal battery storage requirements are generally < 55 kWh, with exception of the energy storage required for solar at the highest latitude location, which exceeds 175 kWh. I contextualize these results by performing a sensitivity analysis of key model parameters and identifying the potential economic impact of future technology advancements. Overall, our results indicate that the financial suitability of the resources evaluated in our study—solar, wind, wave and diesel—vary foremost by factors involving geographical location and resource availability, as well as factors that influence the cost of vessel intervention. This analysis establishes a foundation for future exploration of hybrid generation solutions, which may help to further optimize the generation and storage capacities required for persistent ocean observation. Another approach for reducing these capacities, involving resource forecasting, is explored in the subsequent chapter. In Chapter 3, I consider an ocean observation system that also uses in-situ wave energy generationand battery storage, but meets flexible loads for oceanographic instrumentation and uses wave forecasts to manage this load in real time. By using wave forecasts to optimize power consumption, I hypothesize that it may be possible reduce the size of WEC and battery bank needed, thereby reducing the overall system’s cost and complexity. I model an observation platform that can switch between four power states (full power: 600 W, medium power: 450 W, low power: 45 W, and survival mode: 1 W). To determine which power state to enter over time, I present a stochastic optimization method that interprets wave forecasts and system information to select a power state on an hourly basis, and simulate over two months of consecutive decisions. Using this simulation framework, I compare eight power management strategies across a range of WEC sizes (3 - 5 meter diameter) and battery capacities (2.5 - 35 kWh). I find that it is possible to maintain full (600 W) power consumption over the entire simulation window with a 5 meter diameter WEC and 25 kWh battery bank, which is on the upper end of the ranges evaluated. To employ a smaller WEC or battery, load flexibility is required. Forecast-based methods for this power management handle tradeoffs in performance (e.g., cumulative power consumption, reducing intermittencies and meeting scheduled load targets) more effectively than power management strategies that do not use forecasting. Overall, our results indicate that forecasting for wave-powered ocean observation is most impactful in handling these tradeoffs when some loads must be met on or within a defined schedule. Although not described in the body of this dissertation, I adapted the analytical tools described in Chapters 2 and 3 to clarify how a tribal community may consider exercising its sovereign interests and authorityover its marine space by using locally-available wave energy resources to address their priorities related to emergency preparedness, environmental change and freshwater shortages. In my concluding remarks, I discuss this project at a high level and recommend that, in addition to the characteristics and technology opportunities identified in Chapters 2 and 3, transdisciplinary and community-based approaches that empower denigrated populations and ecosystems are needed to truly “optimize” off-grid wave energy.