With significant development potential available at small run-of-river sites and non-powered dams in the U.S., the authors propose a software program to help interested parties choose the best type of technology to install at these sites.
By Qin Fen (Katherine) Zhang, Patrick O’Connor, Scott DeNeale and Rocio Martinez
This article has been evaluated and edited in accordance with reviews conducted by two or more professionals who have relevant expertise. These peer reviewers judge manuscripts for technical accuracy, usefulness, and overall importance within the hydroelectric industry.
Recent resource assessments have shown that more than 90 GW of small run-of-river and non-powered dam hydropower potential is undeveloped within the conterminous U.S.1,2 Cost-effectiveness remains a large barrier to developing small and low-head hydropower, and the long lead time and risk associated with permitting and licensing further deters deployment. However, innovations in small hydro technologies have emerged in recent years, and most are targeted to addressing high costs and environmental issues. Examples include modular units such as CleanPower’s Turbinator,3 the Natel Energy SLH hydroEngine,4 the MJ2 Very Low Head Turbine,5 and various implementations of the Archimedes screw turbine.6,7 It is important to properly assess and select the best from multiple traditional and novel technologies to reduce investment risk for the project owner.
However, hydro technologies can ultimately be assessed only under site-specific conditions (i.e., site feasibility assessment), and the assessment approach depends on the availability of data pertaining to the site. As a project progresses, increasingly detailed data become available, enabling a more detailed modeling approach. This article introduces different levels of modeling approaches, used at different phases of project development, for evaluating and selecting small hydro technology at potential development sites. In light of the limitations of using existing modeling frameworks, the argument is made for developing a new modeling framework that incorporates the small hydro engineering process into the project life cycle cost-benefit analysis.
Minimum level of assessment — Cost performance indicators
At the earliest stages of project development — even at a screening or permitting level of analysis — detailed site-specific information (hydrologic, geographic, geologic or environmental) may not be readily available. This makes detailed engineering and project value assessments impossible. In this case, only high-level cost performance indicators — including installation cost in $/kW and levelized cost of energy (LCOE) in $/MWh — would be estimated based on major parameters of project characterization (e.g., plant capacity, hydraulic head, project lifespan and capacity factor).
Even with limited site-specific condition and design data, differences in the cost-effectiveness of small hydropower technologies may be apparent if the cost and performance estimates are supported by hydro industry expertise. Figure 1 on page 38 compares project cost and design parameters, based on data from Federal Energy Regulatory Commission (FERC) license documents for all the technologies under discussion, between a Turbinator project and other existing dam or conduit projects with similar capacity and hydraulic head. For the project using the Turbinator technology from Norway, the installation cost is relatively high compared with the other projects. But LCOE values appear to follow similar patterns with respect to capacity and head variations (e.g., the installation cost per kW and LCOE are generally lower for projects with larger capacity or higher head).
LCOE also captures the potential tradeoffs that can occur when using new technology concepts that lower up-front costs at the expense of a reduced project lifetime, such as the modular powerhouse configurations that were once proposed for use on a series of navigation locks on the Mohawk River. As Figure 2 shows (see page 38), compared with estimates from hydropower consultants (A1 and A4) and FERC license document (Marseilles) for traditional lock and dam hydro projects of similar size, the Mohawk Lock modular powerhouse project may have substantially lower installation costs. However, because of the shorter design lifespan of the powerhouse (20 years), LCOE is not as much lower, although LCOE follows similar patterns with respect to head and capacity variations.8
Typically, for both new and traditional technologies, initial capital cost is the key driver of LCOE for a hydro project. However, the initial capital cost alone cannot fully represent the cost performance of a project or technology. LCOE, which takes into account lifetime cost and generation, is a more useful indicator of cost-effectiveness. This is because, even using a simplified analysis model, LCOE calculations explicitly or implicitly account for the primary hydro technology performance metrics, which include: 1) installation cost including environmental mitigation cost, 2) efficiency of power generation, 3) reliability (frequency and cost of outages the technology causes), and 4) lifespan (durability). Yet, because both initial capital cost and capacity factor are uncertain during the earliest phases of project development, using a systematic procedure to produce LCOE ranges that capture the uncertainty would further aid technology selection.
Preliminary assessment modeling
As project development progresses, more hydrologic data, site features and environmental attributes become available. Thus, tools that can incorporate site-specific information into an evaluation methodology provide more accurate estimates of cost and performance with less uncertainty. A handful of industry tools exist at this level of assessment to provide initial site-specific economic estimation, including the widely used Renewable Energy Technology Screen (RETScreen) model9 and methodologies developed by the U.S. Department of Interior’s Bureau of Reclamation (USBR)10 and U.S. Army Corps of Engineers (USACE)11 to assess the economic feasibility of adding hydropower generators to existing unpowered water resource infrastructure.
Differences exist among the hydro tools, models and methodologies, but in general they follow a similar approach. Typically, the tools select an appropriate turbine technology and determine turbine design parameters and efficiencies based on the flow and head duration curves generated using input flow and head time series data (or directly input flow duration curves). With this information and other basic site and project information, capacity and energy generation can be estimated, allowing for project cost estimation and economic analysis. Various economic measures — such as internal rate of return, net present value, benefit-cost-ratio and LCOE — can help inform managers and investors regarding a project’s economic viability. The RETScreen and USBR methodologies are both embedded into publicly available Excel workbooks for use by the wider hydropower community.
One key feature common among hydro project evaluation tools is the conformance to conventional technology and design paradigms, which prevents the evaluation and/or selection of emerging small hydropower technologies. As a step toward allowing new technologies into the preliminary assessment phase of project evaluation, Oak Ridge National Laboratory has developed an additional Excel tool for use in preliminary assessment, the hydropower energy and economic preliminary assessment tool, or HydroPAT.12 HydroPAT merges elements of the performance estimation and costing methodologies applied in RETScreen and the USBR HydroAssessmentTool, while adding the cost and performance characteristics of near-commercial new hydropower technologies, including Natel’s SLH hydroEngine and the AS Clean Power Turbinator. To assess the relative economics of these new small hydro technology applications, HydroPAT has been applied and validated in the assessment of small hydro development opportunities in the Deschutes basin in Oregon.12
A key limitation of these preliminary modeling tools is their inability to explicitly resolve many of the key design parameters that drive the cost of hydropower development, requiring that costs be determined based on a combination of statistical cost equations and rules of thumb. However, many emerging hydro generating technologies have dynamic cost impacts, including impacts on powerhouse design, generating efficiency, and interactions with other technologies (e.g., eliminating the need for a downstream fish passage by using a “fish friendly” turbine). To ensure the cost implications of technology and design are fully captured, a more comprehensive assessment approach to small hydro technology selection than is available from the USBR, ORNL and RETScreen tools is required.
Integrating design and economic assessment
Assessing the impacts from implementation of a new technology is not simple, as the small hydro project design process is highly intricate and complex. For example, there often may be more than one type of turbine technology that is technically suitable for installation at a site, meaning a decision must be made based on economic performance. However, no tools exist to evaluate trade-offs among different technology options. The trade-offs among different components of a hydropower plant (e.g., equipment or civil works costs), between initial capital costs and operation and maintenance costs, or between lifecycle costs and revenues (e.g., related to project capacity, efficiency, and energy production) can make an initial comparison difficult. Often, new technology can be more expensive than traditional alternatives, but secondary impacts on plant system design and generating performance make the novel approach a superior economic choice.
One example of these trade-offs in system design is the recently developed Alden fish-friendly turbine. At some sites, the Alden turbine captures energy from flows that are otherwise spilled for downstream passage and eliminates the need for additional fish protection equipment, the cost of which can vary widely by site and fish species. The Alden turbine is larger in size for its generating capacity than a comparable Francis or Kaplan turbine, resulting in a fully-installed equipment price roughly 35% to 40% higher than what would be expected for these technologies in a similar application. However, this direct cost increase could potentially be offset by reduced civil costs — in a site-specific application, the higher setting relative to the tailwater elevation of the Alden turbine has been estimated to reduce excavation and civil costs by 11%.13
In short, a plant performance evaluation and system design are necessary to capture the full range of economic impacts resulting from application of a technology, and understanding how emerging small hydro technologies are applicable across U.S. hydropower resources requires a holistic approach to modeling plant design and costs. Aside from technology or turbine type selection, lifecycle cost tradeoffs also exist in the conventional determination of the number of units and turbine sizes. Choice of unit sizing has non-linear impacts on turbine efficiencies and the depth of the turbine setting. Choice of number of units has non-linear impacts on generation (e.g., ability to generate across a range of flows) and powerhouse layout and design. The feedback between these implications and costs introduces substantial complexity into the assessment of project feasibility and optimal design.
Because of these complexities, engineering design elements must be incorporated into a project economic analysis framework to form an “integrated model” or “system model,” which is necessary for properly selecting and sizing new technologies and providing preliminary evaluations of site feasibility. Essentially, an integrated model framework optimizes plant economics by minimizing cost and maximizing value over the project lifecycle. At minimum this requires: explicit resolution of project layout and design (including sizing of the conveyance system, powerhouse and generating equipment); plant operational simulation and generation optimization; and flexible cost-benefit analysis based on hydrologic, geographic, geologic, environmental and power market information.
An integrated model is not a silver bullet for hydropower project site assessment — a model cannot fully simulate the expert decision-making abilities of experienced engineers and cannot account for the risks of using new and unproven technologies. The performance of these new technologies must be tracked through time and accurately represented. However, the integration of project layout and design, plant performance and economic assessment can point the way toward more efficient technology selection and initial project feasibility assessment and provide a research framework to assess how new technologies could affect the cost-effectiveness of small hydropower development.
Summary and discussion
In summary, tried-and-true models and methods of hydropower project assessment have been used extensively to identify and evaluate hydropower development opportunities. However, the emergence of dynamic new technologies requires improved analytical capabilities to properly assess development prospects. A next-generation modeling framework is ideal for evaluating and selecting these new small hydro technologies.
The authors are working to develop a prototype modeling application capable of meeting these goals, but industry feedback is necessary and will be heavily sought once prototype development is completed in 2015. What should not be lost in this discussion is the fact that models are useful but may sometimes become dangerous because they both mimic and distort the reality they represent. A model always includes some underlying assumptions, so a balance must be found between simplicity and accuracy, based on data availability. To this end, it is envisioned that an integrated modeling framework would serve only as a research tool and an aid to developers in the early stages of project definition to explore potential design options, providing useful insights to the final engineering-based assessments with ideal project layout and design.
1Hadjerioua, B., Y. Wei, and S.C. Kao, “An Assessment of Energy Potential at Non-powered Dams in the United States,” Oak Ridge National Laboratory, Oak Ridge, Tenn., 2012.
2Hadjerioua, B., et al. “An Assessment of Energy Potential from New Stream-reach Development in the United States: Initial Report on Methodology,” ORNL/TM-2012/298, Oak Ridge National Laboratory, Oak Ridge, Tenn., 2013.
3Opsahl, E., “CleanPower Turbinator: Hydropower without a Dam,” Proceedings of HydroVision International 2013, PennWell Corp., Tulsa, Okla., 2013.
4Schneider, A., “Natel Energy’s Schneider Linear Hydroengine (SLH): Innovation in Low-head Hydropower,” Proceedings of HydroVision International 2010, PennWell Corp., Tulsa, Okla., 2010.
5O’Neil, C., “Hydro Turbine Generating Set for Very Low Head,” Proceedings of HydroVision International 2010, PennWell Corp., Tulsa, Okla., 2010.
6“Screw Generator,” Spaans Babcock Inc., Barrie, Ontario, Canada, 2014.
7“Hydropower Turbines: Archimedian Screw,” Renewables First, Gloucestershire, United Kingdom, 2014.
8Zhang, Q., B. Smith, and W. Zhang, “Small Hydropower Cost Reference Model,” ORNL/TM-2012/501, Oak Ridge National Laboratory, Oak Ridge, Tenn., 2012.
9RETScreen International: Small Hydro Project Analysis, Minister of Natural Resources Canada 2001—2004, National Resources Canada, 2004.
10“Hydropower Resource Assessment at Existing Reclamation Facilities,” U.S. Department of the Interior, Bureau of Reclamation, Power Resources Office, Denver, 2011.
11“Hydropower Resource Assessment at Non-Powered USACE Sites, Hydropower Analysis Center, U.S. Army Corps of Engineers, 2013.
12Zhang, Q., R. Martinez, and B. Saulsbury, “Technical and Economic Feasibility Assessment of Small Hydropower Development in the Deschutes River Basin,” ORNL/TM-2013/221, Oak Ridge National Laboratory, Oak Ridge, Tenn., 2013.
13Dixon, D., and R. Dham, “Fish-Friendly Hydropower Turbine Development and Deployment: Alden Turbine Preliminary Engineering and Model Testing—Supplemental Research,” final technical report, Electric Power Research Institute, Palo Alto, Calif., 2012.
Katherine Zhang, PhD, P.Eng., is a mechanical and hydraulic engineer, Patrick O’Connor is research staff, Scott DeNeale is a post-masters research assistant, and Rocio Martinez is an economist with Oak Ridge National Laboratory.
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