3E1: Cost Analysis to Support NPD Hydropower Development: A Reference System Approach
July 13, 2022
New Development (including Small Hydro)
Hydropower is the oldest renewable source of electricity generation in the US, contributing a total capacity of about 91 GW to the national grid. However, the development of new hydropower projects has slowed down over the last few decades as the capacity potential at prospective sites became smaller and environmental requirements became stricter. This increased the cost per kW of new projects relative to existing large hydropower and newer renewables resources, such as wind and solar which have more abundant untapped competitive resources. Still, new hydropower developments are key to meeting future US global electricity needs. The demand for clean sources of electricity, such as hydropower, is expected to increase due to ambitious carbon emission reduction goals and increasing electricity demand for electric vehicles, as well as other energy uses. In addition, the intermittent nature of wind and solar electricity generation may require large scale storage capacities for which pumped storage hydropower is well suited. Innovations to reduce costs and address environmental requirements are needed to accelerate new US hydropower project development from non-powered dam (NPD) and new stream development (NSD) resources. Apart from issues with smaller capacity potentials and stricter environmental regulations, these resources are also characterized by high levels of variability in water conditions and site features which have a significant effect on development costs. For example, there is an estimated 90,000+ remaining NPD sites in the US, which is more than 30 times the number of existing hydropower sites. This study addresses the cost of developing hydropower from the variety of US NPD sites using a Reference System approach. The method involves three main steps: 1) Characterizing hydropower resources using available data on site features and resources (flow and head). This step partitions about 3,000+ NPD sites that were previously identified as having the largest capacity potentials into twenty 20 Reference groups based on these data. A clustering method was used to ensure that sites within each group are more similar to each other relative to other groups; 2) Baseline design and initial capital cost (ICC) assessments were performed for the representative site, known as Reference site, for each group; 3) A detailed simulation of hydropower generation and cost performance was performed for each Reference site. The simulations accounted for the co-dependence of flow and head variation at NPD sites based on available water resources data. Measures of performance estimated from the simulations include, total capacity, capacity factor, levelized cost of electricity (LCOE), among others. Generation performance was maximized at 200 half-percentiles of the flow/head duration curves to estimate these measures using the ORNL small Hydropower Integrated Development and Economic Analysis (smHIDEA) model. The potential of near-term turbine-generation and civil works innovations to reduce costs were also evaluated. Results of the analysis provide estimates of cost and generation performance over the wide range of potential US NPD hydropower resources, finding LCOE of about $80/MWh at the most competitive Reference site up to one-order of magnitude larger at the least competitive site. The results also provide insights into the different drivers of NPD hydropower costs under Baseline conditions. Potential cost reductions from innovations were estimated to be about 14% to 70% depending on the Reference site and potential innovations, with similar effects on the LCOE. Detailed results from the Reference sites analyses are being used to produce empirical equations for the remaining 3,000+ sites originally considered in the analysis. Efforts to confirm the near-term innovations evaluated in the study and identify potential long-term innovations are being researched for consideration in future NPD cost simulations and empirical modeling.