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DOE OSTI2017

PDCI Wide-Area Damping Control: PSLF Simulations of the 2017 Test Plan - Dual Export Case

To demonstrate and validate the performance of the wide-area damping control system, the project plans to conduct closed-loop tests on the PDCI in spring/summer 2017. A test plan details the open and closed loop tests to be conducted on the PDCI using the wide-area damping control system. To ensure the appropriate level of preparedness, simulations were performed in order to predict and evaluate any possible unsafe operations before hardware experiments are attempted. This report contains the results from these simulations using the power system dynamics software PSLF (Power System Load Flow, trademark of GE). The simulations use the WECC (Western Electricity Coordinating Council) 2016 light summer and heavy summer base cases and the 2014 dual export base case. Because of the large volume of plots, the results were divided into three reports corresponding to the three base cases. This report contains results from the 2014 dual export base case.

25 ENERGY STORAGE↗

DOE OSTI2020

Attack-resilient algorithms and testbed federation for wide-area protection and control in smart grid

Today’s energy infrastructure is undergoing a massive transformation across all generation, transmission, and distribution systems to provide reliability, efficiency, and sustainability to the power system network. During the past 10 years, there has been a remarkable enhancement in developing close-loop wide-area protection and control (WAPAC) applications, such as automatic generation control (AGC), wide-area protection scheme (WAPS), synchrophasor-based wide-area voltage control system (WAVCS), etc., in the energy management system (EMS) to provide an adequate situational awareness to control center operators during physical disturbances and natural events. The rapid digitalization, unencrypted communication, and numerous data-sharing devices have increased digital access points that have exposed the grid network to countless cyberattacks pertinent to the WAPAC cybersecurity. Given the amount of complex grid network coupled with legacy infrastructures, it is untrustworthy to completely rely on information technology (IT)-based conventional security measures to defend against evolving cyberattacks. Hence, there is a compelling need to go beyond the traditional paradigm of ‘security by obscurity’ and ‘bolt-on’ security measures, and develop a suite of innovative security solutions at multiple layers to transform the current “fault-resilient” grid into “fault and attack-resilient” grid of the future. This dissertation specifically focuses on developing application-specific attack-resilient solutions for critical WAPAC applications in the smart grid. The first part of the study, Attack-Resilient Wide-Area Protection Scheme, presents a cyber-physical attack-resilient system (CPARS) for WAPS, where two anomaly detection methods are proposed to detect multi-level cyberattacks followed by the rules-based intrusion mitigation system (RIMS) to initiate effective mitigation strategies, tailored to these attacks, to restore the normal grid operation after disturbances. The first anomaly detection component, Machine learning-based Anomaly Detection for Centralized WAPS, discusses a novel architecture and methodology for developing a machine-learning-based anomaly detection system (ADS) for the centralized wide-area protection system (CWAPS) by applying the variational mode decomposition (VMD) technique and decision tree (DT) algorithms using cyber logs and synchrophasor measurements. The second component, Multi-Agent-based Anomaly Detection for Decentralized WAPS, presents a two-level hierarchical multi-agent-based decentralized WAPS against the system-aware stealthy cyber-attacks. Finally, for the RIMS, the state transition diagram-based mitigation measures are defined based on the grid operation states and experimental testing and evaluation are presented in a cyber-physical testbed environment. The second part of the study, Attack-Resilient Wide-Area Voltage Control System, addresses the existing vulnerabilities in the wide-area voltage control system (WAVCS), presents the methodology for performing an impact analysis during data integrity attacks, and proposes a data-driven attackresilient system (DARS) by incorporating machine learning-based ADS and rules-based anomaly mitigation system (RAMS) to detect these attacks and provide necessary mitigation actions to maintain the transient voltage stability after disturbances. Finally, the third part, Networked Federation Testbed (NEFTSec) for Smart Grid Cybersecurity, focuses on developing an interconnected federated testbed through a common network like the internet, describes its conceptual architectures, and also presents its design components for experimental testing, validation, and evaluation. Further, it presents the co-simulation interface algorithm (CIA) to facilitate the geographically-dispersed real-time distributed simulation (GD-RTDS) during the cyber-physical federation. In general, this dissertation presents innovative attack-resilient solutions for WAPAC applications that are experimentally tested and evaluated using the PowerCyber testbed and NEFTSec platform to accelerate the transition of state-of-the-art research works to the real-field deployments.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Chain-End Controlled Depolymerization Selectivity in α,α-Disubstituted Propionate PHAs with Dual Closed-Loop Recycling and Record-High Melting Temperature

In this article, within the large poly(3-hydroxyalkanoate) (PHA) family, C3 propionates are much less studied than C4 butyrates, with the exception of α,α-disubstituted propionate PHAs, particularly poly(3-hydroxy-2,2-dimethylpropionate), P3H(Me) 2 P, due to its high melting temperature (T m ~ 230 °C) and crystallinity (~76%). However, inefficient synthetic routes to its monomer 2,2-dimethylpropiolactone [(Me) 2 PL] and extreme brittleness of P3H(Me) 2 P largely hinder its broad applications. Here, we introduce simple, efficient step-growth polycondensation (SGP) of a hydroxyacid or methyl ester to afford P3H(Me) 2 P with low to medium molar mass, which is then utilized to produce lactones through base-catalyzed depolymerization. The ring-opening polymerization (ROP) of the 4-membered lactone leads to high-molar-mass P3H(Me) 2 P, which can be depolymerized by hydrolysis to the hydroxyacid in 99% yield or methanolysis to the hydroxyester in 91% yield, achieving closed-loop recycling via both SGP and ROP routes. Intriguingly, the chain end of the SGP-P3H(Me) 2 P determines the depolymerization selectivity toward 4- or 12-membered lactone formation, while both can be repolymerized back to P3H(Me) 2 P. Through the formation of copolymers P3H(Me/R) 2 P (R = Et, n Pr), PHAs with high tensile strength and ductility, coupled with high barriers to water vapor and oxygen, have been created. Notably, the PHA structure–property study led to P3H( n Pr) 2 P with a record-high T m of 266 °C within the PHA family.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Model Predictive Control for Urban Traffic Signals with Stability Guarantees

Traditional traffic signal control focuses more on the optimization aspects whereas the stability and robustness of the closed-loop system are less studied. This paper aims to establish the stability properties of traffic signal control systems through the analysis of a practical model predictive control (MPC) scheme, which models the traffic network with the conservation of vehicles based on a store-and forward model and attempts to balance the traffic densities. More precisely, this scheme guarantees the exponential stability of the closed-loop system under state and input constraints when the inflow is feasible and traffic demand can be fully accessed. Practical exponential stability is achieved in case of small uncertain traffic demand by a modification of the previous scheme. Simulation results of a small-scale traffic network validate the theoretical analysis.

ADVANCED PROPULSION SYSTEMS,MATHEMATICS AND COMPUT↗

DOE OSTI2017

Simultaneous feedback control of plasma rotation and stored energy on NSTX-U using neoclassical toroidal viscosity and neutral beam injection

In this study, a model-based feedback system is presented enabling the simultaneous control of the stored energy through β n and the toroidal rotation profile of the plasma in National Spherical Torus eXperiment Upgrade device. Actuation is obtained using the momentum from six injected neutral beams and the neoclassical toroidal viscosity generated by applying three-dimensional magnetic fields. Based on a model of the momentum diffusion and torque balance, a feedback controller is designed and tested in closed-loop simulations using TRANSP, a time dependent transport analysis code, in predictive mode. Promising results for the ongoing experimental implementation of controllers are obtained.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

Closed-loop optimization of fast trapped-ion shuttling with sub-quanta excitation

Shuttling ions at high speed and with low motional excitation is essential for realizing fast and high-fidelity algorithms in many trapped-ion-based quantum computing architectures. Achieving such performance is challenging due to the sensitivity of an ion to electric fields and the unknown and imperfect environmental and control variables that create them. Here we implement a closed-loop optimization of the voltage waveforms that control the trajectory and axial frequency of an ion during transport in order to minimize the final motional excitation. The resulting waveforms realize fast round-trip transport of a trapped ion across multiple electrodes at speeds of 0.5 electrodes per microsecond (35 m∙s -1 for a one-way transport of 210 μm in 6 s) with a maximum of 0.36 ± 0.08 mean quanta gain. This sub-quanta gain is independent of the phase of the secular motion at the distal location, obviating the need for an electric field impulse or time delay to eliminate the coherent motion.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2024

Chapter 7: Learning Stable Local Volt/Var Controllers in Distribution Grids

This chapter describes a framework to synthesize provably stable local Volt/Var controllers for distributed energy resources (DERs) in power distribution grids (DGs). The goal is to control the reactive power injections of DERs to improve the system performance as quantified by a generic optimal reactive power flow (ORPF) problem. To achieve this, we jointly design for each DER the control function, which prescribes the reactive power update rule, and the equilibrium function, which approximates the ORPF solutions from local measurements of voltages and powers. We provide conditions on the equilibrium functions and the control parameters ensuring the stability of the closed-loop system. In particular, we discuss the trade-offs between each set of conditions accounting for practical considerations, like fully exploiting the DERs' generation capabilities and reducing the optimality gap. These conditions are then translated into learning constraints on the neural networks' parameters that are enforced in the training phase. We validate our framework with numerical simulations on the IEEE 37-bus network and through a comparison with an optimized version of standard piece wise linear control rules.

closed-loop asymptotic stability↗

DOE OSTI2015

Development of intelligent instruments with embedded HTTP servers for control and data acquisition in a cryogenic setup—The hardware, firmware, and software implementation

The power of Ethernet for control and automation technology is being largely understood by the automation industry in recent times. Ethernet with HTTP (Hypertext Transfer Protocol) is one of the most widely accepted communication standards today. Ethernet is best known for being able to control through internet from anywhere in the globe. The Ethernet interface with built-in on-chip embedded servers ensures global connections for crate-less model of control and data acquisition systems which have several advantages over traditional crate-based control architectures for slow applications. This architecture will completely eliminate the use of any extra PLC (Programmable Logic Controller) or similar control hardware in any automation network as the control functions are firmware coded inside intelligent meters itself. Here, we describe the indigenously built project of a cryogenic control system built for linear accelerator at Inter University Accelerator Centre, known as “CADS,” which stands for “Complete Automation of Distribution System.” CADS deals with complete hardware, firmware, and software implementation of the automated linac cryogenic distribution system using many Ethernet based embedded cryogenic instruments developed in-house. Each instrument works as an intelligent meter called device-server which has the control functions and control loops built inside the firmware itself. Dedicated meters with built-in servers were designed out of ARM (Acorn RISC (Reduced Instruction Set Computer) Machine) and ATMEL processors and COTS (Commercially Off-the-Shelf) SMD (Surface Mount Devices) components, with analog sensor front-end and a digital back-end web server implementing remote procedure call over HTTP for digital control and readout functions. At present, 24 instruments which run 58 embedded servers inside, each specific to a particular type of sensor-actuator combination for closed loop operations, are now deployed and distributed across control LAN (Local Area Network). A group of six categories of such instruments have been identified for all cryogenic applications required for linac operation which were designed to build this medium-scale cryogenic automation setup. These devices have special features like remote rebooters, daughter boards for PIDs (Proportional Integral Derivative), etc., to operate them remotely in radiation areas and also have emergency switches by which each device can be taken to emergency mode temporarily. Finally, all the data are monitored, logged, controlled, and analyzed online at a central control room which has a user-friendly control interface developed using LabVIEW{sup ®}. This paper discusses the overall hardware, firmware, software design, and implementation for the cryogenics setup.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2025

Advances in supervisory control strategies for a heat pump centric HVAC system − a comprehensive review on applications

There is an increase in research investigating the development and deployment of supervisory controllers that enable high efficiency electrically driven vapor compression heat pumps operating within grid interactive efficient buildings to provide demand side management. This paper reviews over sixty relevant case studies within this domain that focus on commercial off-the-shelf heat pumps whose primary task is providing space conditioning. The concept of a heat pump-centric heating, ventilation, and air-conditioning system is introduced, accompanied by a detailed overview of various kinds of electric heat pumps and building-level thermal energy storage configurations. Additionally, the different types of supervisory controller designs, including rule-based control and model predictive control, are discussed, along with the various methods for communication between a supervisory controller and a downstream heat pump’s local controller. A comparative analysis is conducted in order to categorize the reviewed case studies based on their system design, supervisory control algorithm, and validation methodology. This detailed analysis allows the review to establish current research trends, identify potential gaps, and suggest future directions for the development of this technology. Overall, the authors recommend that more future research be devoted to low-cost practical retrofits that allow for easy integration of active thermal energy storage within heat pump-centric heating, ventilation, and air-conditioning system systems that utilize direct expansion heat pumps. We also suggest more research into the development and deployment of supervisory controllers that can properly communicate with commercial off-the-shelf heat pump local controller available control inputs, e.g., zone temperature setpoint. Lastly, more rigorous experimental demonstrations of advanced supervisory control within real and or closed-loop, transient/ quasi-steady state environments are necessary to reduce industry wide skepticism of this technology.

Demand side management↗

DOE OSTI2025

Machine learning modeling and model predictive control of a closed-circuit reverse osmosis system

Closed-circuit reverse osmosis (CCRO) offers a flexible and energy-efficient alternative to conventional reverse osmosis by operating in a semi-batch mode that recycles brine, enabling higher recovery rates and reduced specific energy consumption (SEC). However, developing accurate, system-level dynamic models for CCRO remains challenging due to its nonlinear, multi-phase operation and sensitivity to variable feed water conditions. Traditional modeling approaches, such as NARMAX (nonlinear autoregressive moving average with exogenous inputs), often struggle to generalize across varying inlet feed concentrations, necessitating frequent parameter re-estimation and limiting their utility for real-time control applications. To address these limitations, we developed a long short-term memory (LSTM) neural network model trained on an extensive experimental data set from a CCRO pilot plant. The model accepts three inputs, feed flow rate, recirculation flow rate, and initial feed conductivity, and predicts three key outputs: reject conductivity, feed pump power draw, and recirculation pump power draw. We validated the LSTM model against experimental data, demonstrating its ability to distinguish between different feed conductivities and adapt to variable flow rates. Subsequently, we incorporated the LSTM model within a nonlinear model predictive control (MPC) scheme and conducted closed-loop simulations to optimize the integrated SEC (iSEC). In conclusion, the results project up to a 6% reduction in iSEC by using MPC to optimize performance over the entire experiment duration, without requiring any random excitation for data collection or parameter re-estimation.

Desalination↗

DOE OSTI2025

ViBRANT Applied as a Closed Loop Flux and Thermal Surrogate

Surrogate testing platforms provide a means to develop a target aspect of a larger system. Some systems target single aspects while others are more wholistic. ViBRANT (Visual Benign Reactor as Analog for Nuclear Testing) aims to be a wholistic system by representing the primary reactor behaviors through an accessible LED driven reactor core, photodiodes, thermocouples, and high-performance software interfaces being developed to the standards required for actual microreactor deployment such as MARVEL. With the ability to drive control drums enabled with full reactor capability (e.g. speed/angle/torque capability/limits for operation and clutch/spring scram capability), drive ViBRANT’s high fidelity 36 pin hexagon core to represent phenomena via LEDs, and capture the analog response of distributed photodiodes/thermocouples, this paper will show the system’s ability to operate in a closed-loop fashion and do well at matching reactor power/temperature distribution in an accessible reactor surrogate environment.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2021

Nonlinear Multiple Models Adaptive Secondary Voltage Control of Microgrids

This article proposes a model-free secondary voltage control (SVC) for microgrids (MG) using nonlinear multiple models adaptive control. Firstly, a linear robust adaptive controller is designed to guarantee the voltage stability in the bounded-input- bounded-output (BIBO) manner so as to meet the operation requirements of MGs. Secondly, a nonlinear adaptive controller is developed to improve the voltage tracking performance with the help of artificial neural networks (ANNs). A switching mechanism for coordinating such two controllers is designed to guarantee the closed-loop stability while achieving accurate voltage tracking. Here, by an online identification based on the input and output data of MGs, the proposed method does not resort to any apriori information of system model and primary control, thus exhibiting good robustness, ease of deployment and disturbance rejection.

17 WIND ENERGY↗

DOE OSTI2016

Aggregate modeling of fast-acting demand response and control under real-time pricing

This paper develops and assesses the performance of a short-term demand response (DR) model for utility load control with applications to resource planning and control design. Long term response models tend to underestimate short-term demand response when induced by prices. This has two important consequences. First, planning studies tend to undervalue DR and often overlook its benefits in utility demand management program development. Second, when DR is not overlooked, the open-loop DR control gain estimate may be too low. This can result in overuse of load resources, control instability and excessive price volatility. Our objective is therefore to develop a more accurate and better performing short-term demand response model. We construct the model from first principles about the nature of thermostatic load control and show that the resulting formulation corresponds exactly to the Random Utility Model employed in economics to study consumer choice. The model is tested against empirical data collected from field demonstration projects and is shown to perform better than alternative models commonly used to forecast demand in normal operating conditions. The results suggest that (1) existing utility tariffs appear to be inadequate to incentivize demand response, particularly in the presence of high renewables, and (2) existing load control systems run the risk of becoming unstable if utilities close the loop on real-time prices.

Aggregate Modeling↗

DOE OSTI2016

Bounded extremum seeking for angular velocity actuated control of nonholonomic unicycle

Here, we study control of the angular-velocity actuated nonholonomic unicycle, via a simple, bounded extremum seeking controller which is robust to external disturbances and measurement noise. The vehicle performs source seeking despite not having any position information about itself or the source, able only to sense a noise corrupted scalar value whose extremum coincides with the unknown source location. In order to control the angular velocity, rather than the angular heading directly, a controller is developed such that the closed loop system exhibits multiple time scales and requires an analysis approach expanding the previous work of Kurzweil, Jarnik, Sussmann, and Liu, utilizing weak limits. We provide analytic proof of stability and demonstrate how this simple scheme can be extended to include position-independent source seeking, tracking, and collision avoidance of groups on autonomous vehicles in GPS-denied environments, based only on a measure of distance to an obstacle, which is an especially important feature for an autonomous agent.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2021

Deep Learning Explicit Differentiable Predictive Control Laws for Buildings

We present a differentiable predictive control (DPC) methodology for learning constrained control laws for unknown nonlinear systems. DPC poses an approximate solution to multiparametric programming problems emerging from explicit nonlinear model predictive control (MPC). Contrary to approximate MPC, DPC does not require supervision by an expert controller. Instead, a system dynamics model is learned from a small dataset of recorded observations of the perturbed system's dynamics and the control law is optimized offline by interaction with the learned system model. The DPC method is based on two sequential steps, i) system identification using a constrained neural state-space model, and ii) optimization of an explicit control law parametrized by another neural network in closed-loop simulation with the identified neural state-space model. The combination of a differentiable closed-loop system and penalty methods for constraint handling of system outputs and inputs allows us to optimize the control law's parameters directly by backpropagating economic MPC loss through the learned system model. By incorporating domain knowledge and leveraging established techniques from optimal control, our method leverages deep neural networks as nonlinear function approximators for system identification and control while avoiding concomitant costs of intractably large datasets, and computationally expensive over-parametrized models. The scalability, data efficiency, and constrained optimal control capability of the proposed DPC method are demonstrated in simulation using a multi-zone building emulator.

Drgona, Jan↗

DOE OSTI2019

The Velociprobe: An ultrafast hard X-ray nanoprobe for high-resolution ptychographic imaging

Motivated by the advanced photon source upgrade, a new hard X-ray microscope called "Velociprobe" has been recently designed and built for fast ptychographic imaging with high spatial resolution. We are addressing the challenges of high-resolution and fast scanning with novel hardware designs, advanced motion controls, and new data acquisition strategies, including the use of high-bandwidth interferometric measurements. The use of granite, air-bearing-supported stages provides the necessary long travel ranges for coarse motion to accommodate real samples and variable energy operation while remaining highly stable during fine scanning. Scanning the low-mass zone plate enables high-speed and high-precision motion of the probe over the sample. With an advanced control algorithm implemented in a closed-loop feedback system, the setup achieves a position resolution (3σ) of 2 nm. The instrument performance is evaluated by 2D fly-scan ptychography with our developed data acquisition strategies. A spatial resolution of 8.8 nm has been demonstrated on a Au test sample with a detector continuous frame rate of 200 Hz. Using a higher flux X-ray source provided by double-multilayer monochromator, we achieve 10 nm resolution for an integrated circuit sample in an ultrafast scan with a detector's full continuous frame rate of 3000 Hz (0.33 ms per exposure), resulting in an outstanding imaging rate of 9 x 10 4 resolution elements per second

36 MATERIALS SCIENCE↗

DOE OSTI2018

Raman spectroscopy of oxygen carrier particles in harsh environments

Fossil fuel combustion processes generate CO2, a greenhouse gas of considerable concern. To reduce this emission, the National Energy Technology Laboratory (NETL) is evaluating alternative combustion approaches, including chemical looping combustion (CLC). This technique generates relatively pure CO2, suitable for subsequent capture and storage. CLC uses oxygen-carrier particles (OCPs) such as iron oxides, copper oxides, calcium sulfates, etc. to provide oxygen for the combustion process. Optimization of the overall combustion process requires knowledge of the oxidation state (e.g., content of Fe2O3 vs. Fe3O4) of the OCPs during the different stages of the CLC process. Unfortunately, the ability to make on-line measurements of the oxidation state of OCPs in harsh environments is lacking and new sensors need to be developed. We are evaluating non-contact, stand-off Raman spectroscopy to determine the relative concentrations of the oxidized and reduced forms of OCPs at temperatures between 800 °C and 1000 °C, and pressures of about 10 atm. Using cw and pulsed Raman spectroscopy, in combination with a pressurized high-temperature sample chamber, we have optimized the operating parameters such as laser wavelength, laser intensity, collection optic design, focal spot size, etc. and measured Raman spectra of various OCP materials at high temperatures. To extract from the Raman spectra relevant information such as the concentration ratio of a material in different oxidation states, the measured data needs to be processed, and statistical modeling and multivariate calibration need to be performed.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2017

Raman Spectroscopy for the On Line Analysis of Oxidation States of Oxygen Carrier Particles

The need for cleaner energy systems, including CO 2 capture technologies, is driving the current development of new combustion approaches such as chemical looping combustion (CLC). This process is based on using oxygen carrier particles (OCPs) such as iron oxides, copper oxides, calcium sulfates, etc. to provide the oxygen for the combustion process. In order to optimize the overall process performance, it is critical that the properties of the oxygen carriers are well-defined and maintained for their specific purpose during the different stages of the CLC process. One of the critical properties of the oxygen carrier is its oxidation state (e.g., content of Fe 2 O 3 vs. Fe 3 O 4 ) as it affects the fundamental operation of the CLC process. Unfortunately, the ability to make on-line measurements of the oxidation state of oxygen carriers is lacking and new sensors need to be developed. Our goal is to develop a sensor for the on-line analysis of the oxidation state of oxygen carrier particles and to demonstrate its feasibility. Oxygen carrier particles (OCPs) of interest include iron oxides, copper oxides, and calcium sulfate, with particle sizes typically less than 0.5 mm diameter. The expected operating conditions of the sensor include OCP temperatures in the range of 800 °C to 1000 °C, and pressures of about 10 atm. The expected results are to be of a statistical nature, providing relative concentrations of the different oxidation states. These requirements make a strong case for using non-contact, stand-off, pulsed Raman spectroscopy as a sensor for the high-temperature on-line analysis of oxygen carrier particles. In order to achieve our goal, we have set up and tested a pulsed Raman spectroscopy system in combination with a pressurized high-temperature sample chamber, and have taking initial Raman spectra of various materials at high temperature. We are now in the process of optimizing the operating parameters such as laser wavelength, laser intensity, collection optic design, focal spot size, data acquisition, etc. We will also develop an analysis procedure for the interpretation of the Raman spectra. Typical Raman spectra consist of a convolution of background signal and multiple individual spectra. In order to extract relevant information such as the concentration ratio of a material in different oxidation states, we need to process (background subtraction, deconvolution, fitting, etc.) the measured data. In addition, statistical modeling and multivariate calibration need to be performed to correctly analyze the Raman spectra.

20 FOSSIL-FUELED POWER PLANTS↗