Development and Procurement of Major Components for Gen3 CSP Pilot Plant Cooling Loop .
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Uncertainty around the design and control of the supercritical CO2 power cycle must be reduced before this technology can be implemented for large-scale grid support. To better understand the day-to-day performance of an sCO2 cycle, off-design performance calculations must be included for all power block components, and performance assumptions must be removed. This study has expanded the modeled scope to include the air-side performance for the dry cooler and has incorporated discretized heat transfer calculations for both streams through the pre-cooler to better predict off-design performance. This study considered a recompression Brayton cycle in a concentrating solar power application. The cycle model utilized fixed sCO2 turbomachinery maps for the main compressor, recompressor, and expander operating to supply approximately 10 MW gross at the design point. Fixed vendor-supplied fan curves were used to calculate the air-side performance of the dry cooler. The primary heater was modeled considering both the sCO2 and heat transfer fluid streams. Off-design performance was predicted for an ambient temperature range of 0-55℃, a HTF temperature range of 705-735℃, and a HTF mass flow range of 50-105% of the design point value. To understand the importance of modeling the air-side performance, the cycle off-design performance was also calculated using a constant CO2 outlet temperature assumption and a constant approach temperature assumption for the dry cooler. Results show that using these assumptions can significantly alter the power output and cycle efficiency predictions.
An experimental methodology to simulate and characterize the attrition of solid particles used as heat transfer and storage media in third generation concentrating solar power plants was developed and implemented.
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The focused transport theory is appropriate to describe the injection and acceleration of low-energy particles at shocks as an extension of diffusive shock acceleration (DSA). In this investigation, we aim to characterize the role of cross-shock potential (CSP) originated in the charge separation across the shock ramp on pickup ion (PUI) acceleration at various types of shocks with a focused transport model. The simulation results of energy spectrum and spatial density distribution for the cases with and without CSP added in the model are compared. With sufficient acceleration time, the focused transport acceleration finally falls into the DSA regime with the power-law spectral index equal to the solution of the DSA theory. The CSP can affect the shape of the spectrum segment at lower energies, but it does not change the spectral index of the final power-law spectrum at high energies. It is found that the CSP controls the injection efficiency which is the fraction of PUIs reaching the DSA regime. A stronger CSP jump results in a dramatically improved injection efficiency. Our simulation results also show that the injection efficiency of PUIs is mass-dependent, which is lower for species with a higher mass. In addition, the CSP is able to enhance the particle reflection upstream to produce a stronger intensity spike at the shock front. We conclude that the CSP is a non-negligible factor that affects the dynamics of PUIs at shocks.
For solar electricity generating technologies to be cost competitive at a large scale with conventionally generated electricity, cost reductions are needed for both concentrating solar power (CSP) and solar photovoltaic (PV) systems. In 2011, the U.S. Department of Energy (DOE) established solar cost targets that corresponded to reducing CSP and PV prices by approximately 75% in order to achieve a levelized cost of electricity (LCOE) of $0.06 per kilowatt-hour (kWh) for both utility-scale PV and high-capacity factor CSP-TES systems in 2020. Utility-scale PV achieved its 2020 cost target in 2017, and recent estimates for the LCOE of CSP-TES with a molten-salt power tower system are approximately $0.10/kWh for projects that are expected to come online in 2020, which represents a substantial reduction since 2010 - when the LCOE for CSP-TES was around $0.21/kWh. To continue the momentum for cost reductions in solar technologies, DOE recently established cost targets for 2030 that would make solar one of the lowest-cost sources of new electricity in the United States. For CSP-based systems, the new targets correspond to an LCOE in 2030 of $0.05/kWh for a dispatchable, high-capacity factor CSP-TES plant configuration. This aggressive target would have been unimaginable a decade ago. However, building on the previously described reduction in CSP-TES costs over the past decade, recent announcements suggest the next phase of projects will continue this downward trend through lower installation costs, attractive financing, longer-duration PPAs, and the ability to capitalize on the value that the flexibility of storage brings CSP. This presentation summarizes a recent report that evaluates the potential impacts of simultaneously achieving the 2030 cost targets for PV and CSP-TES, and it includes a detailed evaluation of the role that CSP-TES could play in realizing those impacts. The scenarios in this analysis are designed to isolate and assess the potential impacts of achieving DOE's 2030 cost targets for CSP-TES and PV, and they do not reflect the potential benefits or system impacts associated with success in other DOE research programs. The low-cost solar scenarios include DOE's 2030 solar cost targets--which are represented via a roughly 50% reduction in LCOE by 2030 (from current levels) with additional cost reductions thereafter representing technology learning and/or improvements that could result from innovation - coupled with a variety of market, technology, and demand assumptions. With these assumptions, the evolution of the contiguous U.S. electricity system is evaluated with NREL's Renewable Energy Deployment System (ReEDS) model, which was specifically designed to represent the temporal and locational value of renewable generation technologies in the U.S. power system. ReEDS relies on system-wide least-cost optimization to estimate the type and location of future generation and transmission capacity. In addition, it accounts for the locational and temporal variations in variable renewable technologies, including the need for new transmission, curtailment, dynamic capacity value, and the need to hold operating reserves to account for the uncertainty and variability of these technologies. This presentation summarizes the key findings that arise from a detailed evaluation of the impacts of achieving DOE's 2030 cost targets for CSP-TES and PV systems, noting again the inherent challenges associated with modeling future scenarios of the large, complex system electricity system in the contiguous United States within the context of various market features.
Concentrating solar power (CSP) plants convert solar energy into electricity and can be coupled with low-cost thermal energy storage to provide a dispatchable renewable resource. Maximizing the economic benefits of CSP systems requires deliberate selection of the timing of thermal energy dispatch to coincide with high-price or high-value periods. The performance and value of CSP systems have traditionally been evaluated using two distinct approaches: (1) production cost models (PCMs), which optimize commitment and dispatch schedules for an entire fleet of generators to minimize the cost of satisfying electricity demand; and (2) price-taker (PT) models, which optimize dispatch of the CSP system against historical or forecasted electricity prices to maximize revenue available to the CSP operator. The PCM approach is, in principle, superior because of the ability to capture system-level details such as transmission pathways and associated constraints, and feedback between CSP dispatch schedules and operation of other generators. The computationally simpler PT technique cannot explicitly address these concerns, but the comparative reduction in solution time is attractive for sensitivity analysis and rigorous optimization of CSP plant design and operation. In this work, we directly compare the dispatch profiles, net revenue, and operational value of CSP systems using implementations of each technique to assess how closely the solutions from the PT technique can approach those from the PCM for a specific market scenario. The PT implementation produced characteristically similar dispatch profiles as the PCM for both a large solar-multiple configuration and a small solar-multiple peaking configuration, while requiring only 1% of the computational time of the PCM. In addition, dispatching the CSP plant according to the self-scheduled PT solution - as opposed to the system-optimized PCM solution - only reduced the total CSP operational value by 1-5%, with consistent trends across CSP configurations and locations. This analysis validates the ability of the PT approach to substantially capture the economic benefits of CSP systems when pricing profiles are available, and indicates that the PT approach is suitable for initial design and optimization of CSP technologies.
We explore the ability of a concentrating solar power (CSP) plant with thermal energy storage (TES) to provide peaking capacity. We focus on future power systems, wherein net load patterns may be significantly different than they are today (e.g., due to higher renewable-energy penetrations). We examine 28 locations in the southwestern United States over an 18-year period. The hourly operation of the CSP plants are simulated to determine their potential to provide energy during an eight-hour peak-load window for each day up to 365 days per year. Our result shows that for the large majority of locations and years, CSP plants with certain configurations (i.e., in terms of solar field and TES sizes) can provide nearly 100% peak-load capacity. We examine also the amount of supplemental energy (e.g., by using natural gas as a supplemental thermal-energy source) that would be required to ensure that a CSP plant could serve the eight highest-load hours of every day of the year. We find that in most cases, a CSP plant supplemented with natural gas would require less than 5% of the fuel that is used by a natural-gas fired power plant providing the same level of reliable capacity. A series of sensitivity analyses show that these results are robust to the number of peak-load hours and days that are considered and the configuration of the CSP plant.