Multiscale textured solar absorber coatings for next-generation concentrating solar power
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Sibin, K. P. (ORCID:0000000190180073)↗
Engineering topics
Pitchumani, R.
Publications and source records attributed to Pitchumani, R..
Stochastic economic dispatch of wind power under uncertainty using clustering-based extreme scenarios
Operation of power systems with high penetrations of renewable energy sources requires tools for robust decision making under uncertainty. Stochastic economic dispatch and stochastic unit commitment are effective techniques for planning and operation under uncertainty, whose effectiveness depends on the cardinality and quality of the scenario set. Here, this article proposes a machine learning method using -means clustering for capturing relevant physical information from a large population of analog scenarios. Extreme scenario samples drawn from the clusters are used in a two-stage stochastic economic dispatch computation. The effectiveness of the proposed approach is assessed on a synthetic 200-bus system with a geographic footprint over Illinois, USA for four months from each season of WIND Toolkit data. The combination of -means clustering with importance sampling is shown to reduce the total operational cost by over 43% compared to sampling from populations based on heuristic clustering-based methods. Additionally, the variability in the mean cost is about 56% lower than the variability using Monte Carlo sampling. Moreover, the operational cost with the presented approach is shown to be close to the cost calculated based on a hindsight exact wind profile, signifying a highly accurate quantification of wind uncertainty by the presented -means clustering based sampling method.
17 WIND ENERGY↗
Electrodeposited nickel coatings for exceptional corrosion mitigation in industrial grade molten chloride salts for concentrating solar power
Molten chloride salt eutectics are attractive candidates for use as thermal energy storage media and heat transfer fluids in generation-three concentrating solar thermal power (Gen3 CSP) plants. However, corrosion of alloys in molten chloride salts, especially at high temperatures, is an extremely challenging problem that studies focus on lower temperatures, shorter durations, or analytical grade, and high-purity, salts. To date, there has been no study on corrosion or corrosion mitigation in an industrial-grade salt at a high temperature such as 750 °C. To alleviate this knowledge gap, the study presents new multiscale fractal-textured Ni coatings on various alloy surfaces for effective corrosion mitigation at 750 °C in molten chloride salts. Using the electrodeposition method, durable double-layer textured coatings were formed on stainless steel alloys (SS316, SS310, and SS347) and In800H. The corrosion performance of the coatings is investigated in both analytical-grade purity and, for the first time, practically relevant industrial-grade chloride salts. Ni-coated ferrous alloys showed an exceptionally reduced corrosion rate in the range of 350–480 μm/y in analytical-grade salts, and between 450 and 490 μm/y in purified industrial-grade salts at 750 °C. Ni coatings on ferrous alloys reduced corrosion rates by as much as 70% compared to uncoated surfaces and were comparable to the expensive Ha230 alloy with a high Ni content. As a result, by the use of innovative fractal corrosion mitigation coatings, for the first time, low-cost structural alloys are rendered viable for use with industrial-grade chloride salts, which is profoundly beneficial in practical systems.
14 SOLAR ENERGY↗
High-Temperature Air Stability of Electrodeposited Copper Cobalt Oxide and Copper Manganese Oxide Absorber Coatings for Concentrating Solar Power
The solar absorptance of a thermal receiver surface significantly affects the photothermal conversion efficiency of concentrating solar power (CSP) plants. The stability of solar receivers and coatings applied to improve solar absorptance at high temperatures is critical for efficient and long-term operation. This study analyzes the high-temperature air stability of multiscale fractal textured electrodeposited copper manganese oxide and copper cobalt oxide solar receiver coatings over a range of temperatures. The study also investigates the effect of high temperature on phase stability, chemical and morphological evolution, and the optical properties of the two solar absorber materials. A computational model is developed to determine the absorptance of the textured absorbers and is shown to match the experimental measurements closely over a range of heat treatment temperatures from 450 °C to 850 °C and durations up to 100 h. Degradation curves and coating stability maps are developed from the study. Copper manganese oxide and copper cobalt oxide coatings exhibit good thermal stability up to ~815 °C and 630 °C, respectively, with minimal changes in their optical and structural properties with thermal efficiency greater than 90%. Furthermore, the study offers valuable insights into the high-temperature annealing effects on the optical and structural properties of multiscale copper alloyed coatings fabricated by electrodeposition and presents a means of designing the optimal temperature range for maximizing absorber efficiency in practical applications.
14 SOLAR ENERGY↗
Machine learning surrogate of physics-based building-stock simulator for end-use load forecasting
Building energy models are used to simulate heat and mass transfer and estimate end-use load in buildings. With the proliferation of solar photovoltaics on residential and commercial buildings, increasingly, buildings are expected to provide grid services, for which accurate and computationally efficient building energy simulations and end-use load prediction are imperative. Existing building energy simulation tools, however, have significant computational overhead that make them less practical in real-time deployment for optimization, design, uncertainty quantification and control in building energy management systems. Here this article presents a data-driven machine learning model based on light gradient boosting method (LightGBM) as a surrogate for a physics-based simulator for residential buildings to predict end-use load. The machine learning based surrogate model accounts for time-series related variables, seasonality and trend component of end-use load, and history of end-use load. The accuracy of the surrogate model is assessed on the prediction of the load profiles of 100 different houses in Cook County, Illinois, USA. The LightGBM surrogate model is shown to reduce the root-mean-squared error by 53% relative to a reference decision tree (DT) based model reported previously in the literature. Moreover, the model predicts the load spikes and high-ramp rate events throughout the year which are often the Achilles heel of other models in the literature. The machine learning based surrogate model is demonstrated to be computationally efficient, with a ten-fold reduction in the computational time compared to a physics-based building energy simulation, and suitable for uncertainty analysis and real-time control of building characteristics in response to uncertainty.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
A hybrid data-driven and model-based approach for computationally efficient stochastic unit commitment and economic dispatch under wind and solar uncertainty
Stochastic unit commitment (UC) and economic dispatch (ED) are imperative in dealing with uncertainty in renewable forecast for power system operation and planning such that the overall expected production cost is minimized over the planning horizon. However, accurate calculation of the expected production cost requires assessment of a very large number of different scenarios of uncertain renewable resources, such as solar and wind, which is practically infeasible to simulate in real time. This article proposes a hybrid datadriven and physics-based model-predictive paradigm to efficiently solve for stochastic unit commitment and economic dispatch considering uncertainty in wind and solar power forecasts. Here, the novelty of the approach lies in decoupling the production cost estimation from the unit commitment and economic dispatch optimization problems under uncertainty without compromising on the fidelity of the solutions. A data-driven machine learning model is first developed to predict the mean optimal production cost. A physics-based inverse problem is then solved to get the stochastic UC and ED profiles from the expected cost. The presented approach considers, for the first time, solar uncertainty in UC/ED determination and enables efficient and accurate propagation of wind and solar uncertainty to estimate the statistics of the production cost. The effectiveness of the developed approach is demonstrated systematically on a stylized RTS-GMLC single-node system. The overall framework predicts the expected cost 62.5% more accurately than the existing state-of-the-art, on unforeseen days during the entire year, and yields, for the first time, the associated physically consistent UC and ED profiles. The solutions are also shown to be flexible in providing adequate daily reserves to address any statistical deviations from probabilistic power forecasts. The computational time associated with the presented method is only about 10 s compared to over 24 h needed for a conventional stochastic UC/ED determination under uncertainty on an Intel Core i9 processor with 32 GB of RAM.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗