Signature Projects are intended to bring focus to a selection of U.S. Department of Energy's Water Power Technologies Office (WPTO) projects. By designating a Signature Project, the project reports, datasets, and associated papers can be easily discoverable. By bringing together all aspects of a project, whether a completed legacy project or an ongoing investigation, the MRE community can be informed of what investigations have been undertaken, which have succeeded, what tools are available, and where gaps in information persist.
Benchmark the techno-economic performance, i.e., levelized cost of energy (LCOE), of open-source designs of marine hydrokinetic (MHK) technology farms to identify cost drivers and cost-reduction strategies.
The Reference Model Project (RMP), sponsored by the U.S. Department of Energy (DOE), was a partnered effort to develop open-source MHK point designs as reference models (RMs) to benchmark MHK technology performance and costs, and an open-source methodology for design and analysis of MHK technologies, including models for estimating their capital costs, operational costs, and levelized costs of energy. The point designs also served as open-source test articles for university researchers and commercial technology developers. The RMP project team, led by Sandia National Laboratories (SNL), included a partnership between DOE, three national laboratories, including the National Renewable Energy Laboratory (NREL), Pacific Northwest National Laboratory (PNNL), and Oak Ridge National Laboratory (ORNL), the Applied Research Laboratory of Penn State University, and Re Vision Consulting.
Figure 1 illustrates the design methodology, including the iterations needed to meet key constraints imposed for structural design and for environmental compliance (refer to dashed lines from the decision boxes below the ‘Design and Analysis’ and ‘Environmental Compliance’ module boxes shown in the center of the flowchart). The methodology used for the four reference models (point designs) deviates in varying degrees from this idealized methodology. These deviations were due mainly to the limitations on resources available for this initial study. For example, weather windows, one of the inputs under ‘Site Information’ on the chart, were not calculated for the four reference model sites. Where applicable, we reference work done by others who have performed higher order analyses. The methodology centers on four core modules illustrated in Figure 1:
The Design & Analysis (D&A) Module applies engineering models to design, analyze, and optimize power and structural performance for a given MEC device paired with its reference site resource. Output from this module determines the technical feasibility of the device/array and the potential AEP. The final design specifications provide the data needed to determine materials and manufacturing costs in the ‘Manufacturing & Deployment (M&D) Strategy’ Module.
The Manufacturing & Deployment (M&D) Strategy Module delineates the materials and manufacturing processes and deployment strategies that are adopted in order to determine CapEx associated with manufacturing the device and deploying it at different array scales. This includes CapEx for subcomponent materials based on structural analysis of extreme loadings, subsystem requirements to reduce O&M costs, and deployment (installation) costs. Deployment strategies include service vessel requirements and other considerations for the installation of the MEC devices and their associated infrastructure referred to as balance of system (BOS) components—examples would be the mooring components and the transmission cables connecting the device/array to the substation for grid connection.
The Operations & Maintenance (O&M) Strategy Module delineates an O&M strategy and identifies costs based on estimates of subcomponent and subsystem failure rates and service requirements for operations and other OpEx categories. O&M strategies include service vessel requirements to maintain the MEC devices and the array infrastructure. This module also accounts for expected operational availability—this is based on land-based wind plant/farm data—which determines the actual AEP considered in the LCOE estimate.
The Environmental Compliance (EC) Module details the site studies and environmental monitoring needs to meet regulatory siting and permitting requirements for deploying the Reference Model array at a particular reference site. EC costs are mainly CapEx because they occur before deployment and operation. Many monitoring activities, site studies, and related research work are critical for compliance with environmental regulations and permitting requirements (NEPA is the primary driver), addressing stakeholder input, and determining the overall feasibility of deploying the MEC device/array given all discovered factors. Both pre-installation environmental studies and—if deployment at the selected site is found acceptable—post-installation studies will be conducted as well as recurring environmental monitoring that will take place during the lifecycle of the device/array. Recurring, routine environmental monitoring costs after operations begin are treated as OpEx.
These four modules include various sub-modules (not all of which are shown on the flow chart) used for analysis, design iteration, and optimization to meet structural and environmental constraints.
Figure 1. Methodology for design, analysis, and LCOE estimation for MEC technologies (Neary et al. 2014). This figure is intended to be illustratibve and does not capture all details and items covered in the methodology.
Six MHK technology point designs include three current energy converters (CECs) and three wave energy converters (WECs). The LCOE was estimated based on four primary inputs, including CapEx, OpEx, AEP, and FCR. The cost breakdown of the six RM models was analyzed for up to a 100-unit array, and a study on the overall LCOE comparison of the models for 10-MW commercial-scale arrays was conducted (Jenne et al. 2015).
The study demonstrated that CECs (e.g., 10 MW installed capacity) are within the range of early market adoption primarily because the CEC technologies are more mature then WECs. Much of the maturity associated with CEC is a result of the knowledge gained from offshore and land-based wind technology; however, technology advancements that will lead to significant LCOE reductions are still needed to be widely competitive. Cost reductions for CEC devices will most likely result from improving OpEx strategies and reducing PTO cost. WEC devices, on the other hand, are further behind on the market readiness scale, and there is little convergence on a standard WEC technology, particularly with respect to and device size. In addition, the WECs studied in this RM project are most likely overdesigned structurally, as mentioned in the last section. The systems can be further improved by implementing advanced control strategies to optimize their power performance. In addition to the cost reduction from OpEx and PTO, structure design innovation, and power performance improvement are two important areas that need additional research to accelerate WEC technology development to market readiness.