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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Membrane‐electrode assembly design parameters for optimal CO 2 reduction

Commercial-scale generation of carbon-containing chemicals and fuels by means of electrochemical CO 2 reduction (CO 2 R) requires electrolyzers operating at high current densities and product selectivities. Membrane-electrode assemblies (MEAs) have been shown to be suitable for this purpose. In such devices, the cathode catalyst layer controls both the rate of CO 2 R and the distribution of products. In this study, we investigate how the ionomer-to-catalyst ratio (I:Cat), catalyst loading, and catalyst-layer thickness influence the performance of a cathode catalyst layer containing Ag nanoparticles supported on carbon. In this paper, we explore how these parameters affect the cell performance and establish the role of the exchange solution (water vs. CsHCO 3 ) behind the anode catalyst layer in cell performance. We show that a high total current density is best achieved using an I:Cat ratio of 3 at a Ag loading of 0.01–0.1 mg Ag /cm 2 and with a 1.0 M solution of CsHCO 3 circulated behind the anode catalyst layer. For these conditions, the optimal CO partial current density depends on the voltage applied to the MEA. The work also reveals that the performance of the cathode catalyst layer is limited by a combination of the electrochemically active surface area and the degree to which mass transfer of CO 2 to the surface of the Ag nanoparticles and the transport of OH – anions away from it limit the overall catalyst activity. Hydration of the ionomer in the cathode catalyst layer is found not to be an issue when using an exchange solution. The insights gained allowed for a Ag CO 2 R MEA that operates between 200 mA/cm 2 and 1 A/cm 2 with CO faradaic efficiencies of 78–91%, and the findings and understanding gained herein should be applicable to a broad range of CO 2 R MEA-based devices.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Series FACTS Devices for Increasing Resiliency in Severe Weather Conditions

Severe weather conditions are low-probability, high-impact events that affect grid operations. The majority of power outages are caused by severe weather conditions. Grid resiliency to weather events can be enhanced by decreasing the reliance on its affected sections. One way to do this is to reduce the power flow through lines vulnerable to severe weather. If a line is disconnected, its initial power flow is distributed through the neighbor lines, which may cause congestion in the grid. FACTS devices can be used to control the power flow of lines that have a higher chance of power outages. Most previous works do not consider weather events in power flow control. In this work, a linearized optimal power flow (OPF)–based algorithm is developed to minimize the real power flow of vulnerable lines considering the thermal limits of lines to prevent infeasible solutions; the simulation is fast, making it suitable for large-scale systems. The proposed optimization problem is presented as a mixed-integer linear program (MILP), making it capable of using short-term load forecasting due to its high solution speed. The proposed optimization problem considers multiple lines with different outage probabilities and the uncertainties of the weather forecast. Moreover, it estimates the power reduction in vulnerable lines due to changes in the series FACTS devices. The performance of the proposed optimization problem is tested on IEEE 14-, 30-, and 118-bus systems for several scenarios. The results are validated with the AC power flow results from MATPOWER.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Artificial Intelligence-Driven Management of Sustainable Energy Resources: Visibility, Operation, and Control

The rapid global transition toward sustainable energy resources (SERs) is reshaping how modern power systems are observed, optimized, and controlled. While SERs have significantly advanced decarbonization, their weather dependence, variability, and inverter-dominated characteristics challenge traditional, centralized, and deterministic grid operation. At the same time, the proliferation of high-resolution data from inverters, smart meters, and sensors offers unprecedented visibility into system dynamics. Yet, it also exceeds the analytical capability of conventional model-based approaches. Artificial intelligence (AI) provides a new foundation for addressing these challenges by bridging physical laws with data-driven learning, enabling accurate state awareness, adaptive operation, and coordinated control across distributed assets. This article examines how AI transforms the management of SER-rich power systems along three critical dimensions: 1) enhancing visibility by inferring behind-the-meter (BTM) activities, assessing SER flexibility, and reconstructing system states from sparse or noisy measurements; 2) improving operation through AI-enhanced SER service provision, volt/var control (VVC), and dynamic operating envelopes (DOE) for efficiency and security; and 3) advancing control by embedding learning-based intelligence into inverter coordination, voltage and frequency regulation, and long-term dispatch. Together, these developments reveal how AI can convert the variability of SERs from an operational challenge into a source of flexibility, resilience, and intelligence, paving the way toward sustainable, adaptive, and self-optimizing power systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Demonstration of Dimensional and Microstructural Postirradiation Examination Capabilities on MiniFuel Disk Specimens

This work demonstrates two new MiniFuel postirradiation examination (PIE) capabilities, laser profilometry and serial sectioning, for future use on alpha-U specimens for the upcoming Mini-99 irradiation campaign. Representative U–Mo disk specimens—3 mm in diameter and approximately 0.75 mm in thickness—were subjected to dimensional measurements using standard calipers, as well as a new Keyence CL-3000 series laser profilometry system designed to measure the surface of both sides of the disk simultaneously. The laser profilometry system captured thickness gradients across the specimens as large as 0.2 mm. Additionally, because of the limitations inherent to the use of calipers, caliper measurements consistently overestimated the initial volume of MiniFuel disk specimens compared to the laser profilometry method by up to 5%. However, the results show that optimal data collection and analysis procedures are crucial to avoiding significant artifacts during data collection that can lead to large errors (approximately 10%). Serial sectioning using a combination of ion milling and electron imaging was also demonstrated for the analysis of micron-sized control volumes. Nanometer- to micron-scale porosity was resolved on a representative U–Mo specimen before irradiation, even when the surface of the specimen appeared free of porosity. Future efforts to relate microstructural features (porosity, fission product distributions, and so on) to macroscale effects (dimensional changes, thermal conductivity degradation) require accurate characterization methods such as those demonstrated in this work. These advanced PIE capabilities will be applied to optimized alpha-U specimens for the upcoming Mini-99 neutron irradiation campaign.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Curriculum-based Reinforcement Learning for Distribution System Critical Load Restoration

This paper focuses on the critical load restoration problem in distribution systems following major outages. To provide fast online response and optimal sequential decision-making support, a reinforcement learning (RL) based approach is proposed to optimize the restoration. Due to the complexities stemming from the large policy search space, renewable uncertainty, and nonlinearity in a complex grid control problem, directly applying RL algorithms to train a satisfactory policy requires extensive tuning to be successful. To address this challenge, this paper leverages the curriculum learning (CL) technique to design a training curriculum involving a simpler steppingstone problem that guides the RL agent to learn to solve the original hard problem in a progressive and more effective manner. We demonstrate that compared with direct learning, CL facilitates controller training to achieve better performance. To study realistic scenarios where renewable forecasts used for decision-making are in general imperfect, the experiments compare the trained RL controllers against two model predictive controllers (MPCs) using renewable forecasts with different error levels and observe how these controllers can hedge against the uncertainty. Results show that RL controllers are less susceptible to forecast errors than the baseline MPCs and can provide a more reliable restoration process.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Production of Particle Reference and Quality Control Materials

Maintaining the superior performance of new analytical instrumentation and pushing the boundaries of ever-smaller particle analysis by methods such as secondary ion mass spectrometry (SIMS) has become limited by the availability of certified particle standards for calibration, quality control, and validation. To meet this growing demand for a reliable and universal approach to the generation of uranium particle reference material, the Pacific Northwest National Laboratory (PNNL) and Savannah River National Laboratory (SRNL) are collaborating on a joint venture that establishes a number of chemical pathways to the fabrication, purification, and stabilization of uranium particle material to within a fixed particle size range, and well-characterized isotopics. PNNL particle standards are designed and tailored to meet the criteria essential to the calibration and benchmarking of instruments used in both the non-destructive and destructive assay of particulate material, typically collected from environmental swipe sampling. Previous testing and optimization of this colloidal approach to particulate material has centred on tight size distributions, singular composition and density, uniform morphology, and tailored isotopic abundances. Extensive validation of this material has since been performed, both at the National Institute of Standards and Technology (NIST) and the International Atomic Energy Agency (IAEA), demonstrating its suitability for quality control needs of large geometry (LG)-SIMS analysis. Building on this momentum, the production of low-enriched uranium particle reference material, with specially tailored isotopics, has moved from development to full operation. Herein, various aspects of the production cycle will be discussed, including detailed accounts of the technical methodologies being employed, as well as insights into the sample characterization and acceptance testing requirements.

Nuclear Safeguards, Particle Standards, IAEA↗
DOE OSTI2022

Cyber Resilient Design and Controls for Energy Systems

As the grid of today is evolving into a highly distributed and autonomous cyber-physical system with higher penetration of Distributed Energy Resources (DERs) and autonomous controls that are dependent on the cyber infrastructure, there are novel and increasing challenges to cyber-resilient operation of the grid. While the traditional mechanisms take a "bolt on" approach to cybersecurity and resiliency, this talk presents two novel technologies developed at NREL that "bake in" security and resilience into the design and control of the grid. An Adaptive Resilience Metrics framework is a novel mechanism for system owners and operators to understand the impact of cyber and physical system design and topology on the resilient operation, while also helping determine optimal policies in response to adverse cyber events. Along with that a zero-knowledge proof-based technique helps incorporate security into the controls layer by focusing on computational integrity rather than data integrity. By leveraging design and control properties unique to Operational Technology systems these technologies will not only help enhance cyber-resilience for the grid of today, but a highly distributed and autonomous grid of tomorrow.

cyber resilience↗
DOE OSTI2021

Stochastic Pricing Game for Aggregated Demand Response Considering Comfort Level

In recent years, demand response (DR) has been explored as a fundamental strategy for demand-side management due to its advantages in mediating intermittency of renewable energy generation, load shifting, etc. To engage customers in DR programs, several deterministic price-based DR strategies have been developed and implemented. However, the stochastic weather conditions and occupants' consumption behaviors often make the deterministic solution less robust to uncertainties. In this paper, with the consideration of the uncertainties, a stochastic Stackelberg game is proposed to model the price-demand negotiation between a distributed system operator and load aggregators, where the virtual battery constraints are extracted from the building thermostatically controlled loads (TCLs)‘ characteristics to guarantee comfortable TCLs' levels. Following the negotiation, a priority-based control method is used to allocate the optimal aggregated power DR profile at the building level and track the power signal. Several groups of experiments have demonstrated the effectiveness and robustness of the stochastic solutions.

Chen, Yang↗
DOE OSTI2021

A Zonal Volt/VAR Control Mechanism for High PV Penetration Distribution Systems

This paper presents a zonal Volt/VAR control scheme for regulating voltage in unbalanced 3-phase distribution systems using inverter-based resources (IBR). First, the dependency between nodal voltage changes and IBR reactive power injections is derived via voltage sensitivity studies. Then, a fast-incremental clustering method is used to divide the distribution circuit into weakly-coupled zones based on correlations between nodal voltage sensitivities. The weak-coupling allows the voltage to be regulated independently within each zone using a rule-based voltage controller to dispatch IBR for voltage corrections. Simulation results on actual distribution feeder show that the proposed zone-based Volt/VAR control method maintains system voltages within their operational limits while reducing the runtime of the Volt/VAR controller from tens of second to a couple of milliseconds compared with centralized, optimization-based Volt/VAR control methods.

Alrushoud, Asmaa↗
DOE OSTI2020

A Scalable and Distributed Algorithm for Managing Residential Demand Response Programs using Alternating Direction Method of Multipliers (ADMM)

For effective engagement of residential demand-side resources and to ensure efficient operation of distribution networks, we must overcome the challenges of controlling and coordinating residential components and devices at scale. To overcome this challenge, we present a distributed and scalable algorithm with a three-level hierarchical information exchange architecture for managing the residential demand response programs. First, a centralized optimization model is formulated to maximize community social welfare. Then, this centralized model is solved in a distributed manner with alternating direction method of multipliers (ADMM) by decomposing the original problem to utility-level and house-level problems. The information exchange between the different layers is limited to the primary residual (i.e., supply-demand mismatch), Lagrangian multipliers, and the total load of each house to protect each customer’s privacy. Simulation studies are performed on the IEEE 33 bus test system with 605 residential customers. The results demonstrate that the proposed approach can save customers’ electricity bills and reduce the peak load at the utility level without much affecting customers’ comfort and privacy. Finally, a quantitative comparison of the distributed and centralized algorithms shows the scalability advantage of the proposed ADMM-based approach, and it gives benchmarking results with achievable value for future research works.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Fault-Tolerant Decentralized Control for Large-Scale Inverter-Based Resources for Active Power Tracking

Integration of inverter-based resources (IBRs) which lack the intrinsic characteristics such as the inertial response of the traditional synchronous-generator (SG)-based sources presents a new challenge in the form of analyzing the grid stability under their presence. While the dynamic composition of IBRs differs from that of the SGs, the control objective remains similar in terms of tracking the desired active power. This letter presents a decentralized primal-dual-based fault-tolerant control framework for the power allocation in IBRs. Overall, a hierarchical control algorithm is developed with a lower level addressing the current control and the parameter estimation for the IBRs and the higher level acting as the reference power generator to the low level based on the desired active power profile. The decentralized network-based algorithm adaptively splits the desired power between the IBRs taking into consideration the health of the IBRs transmission lines. The proposed framework is tested through a simulation on the network of IBRs and the high-level controller performance is compared against the existing framework in the literature. The proposed algorithm shows significant performance improvement in the magnitude of power deviation and settling time to the nominal value under faulty conditions as compared to the algorithm in the literature.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Deep Learning without Global Optimization by Random Fourier Neural Networks

Here we introduce a new training algorithm for deep neural networks that utilize random complex exponential activation functions. Our approach employs a Markov chain Monte Carlo sampling procedure to iteratively train network layers, avoiding global and gradient-based optimization while maintaining error control. It consistently attains the theoretical approximation rate for residual networks with complex exponential activation functions, determined by network complexity. Additionally, it enables efficient learning of multiscale and high-frequency features, producing interpretable parameter distributions. Despite using sinusoidal basis functions, we do not observe Gibbs phenomena in approximating discontinuous target functions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Autonomous Intelligent Charging/Discharging of Electric Vehicles using Distributed Multi-Agent ADMM Framework for Grid Ancillary Services

The increasing popularity of Electric Vehicles (EVs) in the distribution grid along with technological advancement in EV electronics such as vehicle to grid (V2G) technique has enabled them to participate in grid ancillary services. To achieve this, the EVs need to establish a contract with third-party aggregators and connect to a charging unit, either residential or commercial. At any time they are connected, the EVs can decide to take part in the ancillary services program offered to them by the aggregators. If agreed, the aggregators will use the EVs as a power source capable of charging/discharging power according to the input signal, and in return, they will be compensated. This inter-temporal nature of charging/discharging is also transforming the traditional optimal power flow (OPF) problem into a dynamic OPF problem. This chapter aims at developing a multi-layer time-dependent optimization algorithm to utilize EV potential and provide ancillary services while maximizing its utilization function. Specifically, in the upper layer, an autonomous distributed ADMM algorithm is developed to optimize the cost for charging/discharging EVs while using them to regulate the voltage at each bus in the distribution grid. The distributed ADMM algorithm is also expanded to the lower layer where the individual EVs active and reactive power is controlled for voltage regulation while maintaining the desired state of the charge of the vehicle at the end of the charging period. Here, the effectiveness and performance improvement of the proposed multi-layer algorithm is illustrated through analytical analysis and simulation results.

Rahman, Towfiq↗
DOE OSTI2020

Autonomous Intelligent Charging/Discharging of Electric Vehicles using Distributed Multi-Agent ADMM Framework for Grid Ancillary Services

The increasing popularity of Electric Vehicles (EVs) in the distribution grid along with technological advancement in EV electronics such as vehicle to grid (V2G) technique has enabled them to participate in grid ancillary services. To achieve this, the EVs need to establish a contract with third-party aggregators and connect to a charging unit, either residential or commercial. At any time they are connected, the EVs can decide to take part in the ancillary services program offered to them by the aggregators. If agreed, the aggregators will use the EVs as a power source capable of charging/discharging power according to the input signal, and in return, they will be compensated. This inter-temporal nature of charging/discharging is also transforming the traditional optimal power flow (OPF) problem into a dynamic OPF problem. This chapter aims at developing a multi-layer time-dependent optimization algorithm to utilize EV potential and provide ancillary services while maximizing its utilization function. Specifically, in the upper layer, an autonomous distributed ADMM algorithm is developed to optimize the cost for charging/discharging EVs while using them to regulate the voltage at each bus in the distribution grid. The distributed ADMM algorithm is also expanded to the lower layer where the individual EVs active and reactive power is controlled for voltage regulation while maintaining the desired state of the charge of the vehicle at the end of the charging period. The effectiveness and performance improvement of the proposed multi-layer algorithm is illustrated through analytical analysis and simulation results.

Rahman, Towfiq↗
DOE OSTI2021

Visual Tool for Assessing Stability of DER Configurations on Three-Phase Radial Networks

Here, we present a method and tool for evaluating the placement of Distributed Energy Resources (DER) on distribution circuits in order to control voltages and power flows. Our previous work described Phasor-Based Control (PBC), a novel control framework where DERs inject real and reactive power to track voltage magnitude and phase angle targets. Here, we employ linearized power flow equations and integral controllers to develop a linear state space model for PBC acting on a three-phase unbalanced network. We use this model to evaluate whether a given inverter-based DER configuration admits a stable set of controller gains, which cannot be done by analyzing controllability nor by using the Lyapunov equation. Instead, we sample over a parameter space to identify a stable set of controller gains. Our stability analysis requires only a line impedance model and does not entail simulating the system or solving an optimization problem. We incorporate this assessment into a publicly available visualization tool and demonstrate three processes for evaluating many control configurations on the IEEE 123-node test feeder (123NF).

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Multi-injection investigation of a high-volatility diesel in advanced compression ignition combustion for NO x control

Traditional selective catalytic reduction aftertreatment technologies used to reduce [Formula: see text] are very limited at exhaust temperatures below [Formula: see text]. Therefore, under these low engine load conditions, having effective in-cylinder control of [Formula: see text] emissions is important. Previous work by the authors explored the effect of fuel physical properties on the ability to control [Formula: see text] in-cylinder. That work was limited to one direct injection near top dead center. Modern diesel high-pressure fuel systems have the capability of five or more injections in one engine cycle. A higher-volatility diesel fuel and high amounts of exhaust gas recirculation to delay ignition could provide an opportunity for reduction in engine-out [Formula: see text] through an increased level of fuel premixing. By appropriately timing multiple short injections, a more optimal distribution of fuel in-cylinder may be achieved, which could reduce [Formula: see text] while maintaining an efficient combustion phasing. A computational fluid dynamics model previously validated against experimental data was used to explore several injection strategies with increased levels of fuel premixing to assess the potential trade-offs between [Formula: see text] and CO/unburned hydrocarbon (UHC) emissions and thus reduce reliance on the aftertreatment system for [Formula: see text] control. The results show that the devised injection strategies resulted in an increased level of fuel premixing. However, none of the attempted injection strategies resulted in significant [Formula: see text] reductions, and all strategies showed a significant increase in CO and UHC emissions.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Grid-Connected Modular Soft-Switching Solid State Transformers (M-S4T)

The objective of this project is to develop and verify the concept of a flexible and modular soft-switching solid-state transformer (M-S4T) for direct grid-connected applications. The ability to directly connect power electronics converters to the medium voltage grid (4 kV – 13 kV), and to potentially replace the passive and bulky, but ubiquitous 60 hertz service transformer in the 25 kVA to 100 kVA range, with a more flexible and controllable device, has been regarded as the ‘holy grail’ in grid control. However, this has proven to be extremely difficult. This project has developed the solutions to several key challenges of the direct grid-connected power electronics and realized a 7.2 kV M-S4T prototype. First, a protection method to protect the M-S4T from the high voltages (110 kV for the 13 kV system) that occur on the grid due to transients and lightning strikes have been developed and experimentally verified. Second, the realization and the operation of the M-S4T based on high-voltage SiC devices (>3.3 kV) and a medium-frequency medium-voltage low-leakage transformer in a single-stage solid-state transformer with zero-voltage switching, low dv/dt, and low electromagnetic interference has been successfully demonstrated up to 7.5 kV peak. Third, an oil-cooling system and stable communication and distributed control system for converter module voltage sharing have been developed and experimentally verified. The developed M-S4T has realized a modular universal high-performance power conversion system. This conversion system is scalable to different voltage and power levels and adaptable to four-quadrant bidirectional operation. Moreover, the use of passive cooling techniques meets the equipment life requirements, and the lightning protection scheme fulfills the basic insulation level specifications for direct grid connection. Such power conversion system opens up near-term opportunities, including energy storage, solar PV, or electric vehicle charging with significant cost and footprint savings. In the longer term, the possibility of replacing the utility distribution transformer with an M-S4T will be transformative for future distribution grids with a compact footprint and full controllability to enable high renewable energy and storage penetration. In addition to the main project, this report expands on the Plus-Up projected including as part of the main award. This project developed and demonstrated the technology for autonomous collaborative inverters that can be connected in an ad hoc manner to the grid. The aim of the project was to: (1) evaluate the existing techniques for grid-connected inverters and find their limitations; (2) develop detailed requirements for grid-connected inverters in the modern grid with millions of active nodes; (3) design a unified control strategy that brings more autonomy and intelligence to grid-connected inverters, and addresses parts of the issues with the existing techniques. The proposed technique, called UniCon, enables inverters to 1) connect/disconnect to/from the grid in an ad hoc manner; (2) work based on local sensing. Slow communication could be used for a more optimized behavior; (3) work automatically in both grid-forming/grid-following mode; (4) handle large disturbances, e.g., big load step and fault, in an oscillation-free manner; (5) work collaboratively with other inverters in steady-state and during transients. UniCon can be implemented in the middle-level control; hence it is agnostic to the vendor and to the implementation of the inner voltage/current and protection loops. Furthermore, a new synchronization scheme, based on deep learning, was developed that can extract the grid voltage phase and amplitude in a stable manner. The method is cheap to implement can improve the dynamic performance of the grid-connected inverters during fast transients, e.g., fault. The proposed control scheme was validated by (1) MATLAB/Simulink; (2) hardware-in-the-loop results, and; (3) experimental results using three inverters that form a microgrid in a down-scaled feeder. Lastly, both the M-S4T and UniCon have achieved promising tangible paths to markets. In the case of the M-S4T, the underlying technology — the Soft Switching Solid State Transformer (S4T) developed at the Georgia Tech Center for Distributed Energy (GT-CDE) has been licensed by GridBlock from the Georgia Tech Research Corporation, and GridBlock has been working with manufacturing partner Jabil (one of the largest US-based contract manufacturers) and system integrator Power Secure (largest deployer of microgrids in the US with 4.7 GW under management), to meet the strong initial demand. Similarly, GridBlock has an exclusive license to the UniCon technology, developed under this award by GT-CDE. The UniCon provides an intermediate control layer that enables the implementation of the higher-level ‘transactive’ control commands for the system. The architecture of the system - slow communications with the cloud for system optimization and setpoints, and the use of locally measured quantities for real-time control, provide a very robust and secure way of implementing a real-time must-run grid that is also secure and stable. This is a brand-new functionality that is critical for the future grid and key to GridBlock’s business model.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

A nuclear-hydrogen hybrid energy system with large-scale storage: A study in optimal dispatch and economic performance in a real-world market

Here a study in optimal dispatch and economic analysis of a novel nuclear hybrid energy system (NHES) with large-scale hydrogen storage and a novel control scheme is conducted. The control scheme makes charge and discharge decisions by using the real-time electricity price relative to the historical price distribution. The pricing data is taken from the ERCOT Houston and West load zones with different price oscillations. Six case control studies with different levels of aggressiveness were chosen for each load zone for a total of 12 case studies. It is found that in a high price volatility load zone, a more aggressive control strategy maximizes revenue, while in a less oscillatory load zone, a moderate control strategy performs better. The NHES can store energy when the price is low and sell additional electricity when the price is high, as well as participate in the ancillary services market. Due to these advantages, the NHES can generate 10–40 million USD more revenue annually than a stand-alone nuclear plant. Due to higher costs, the NHES has a higher LCOE (81.36 USD/MWh) than a stand-alone plant (68.66 USD/MWh); however, the LCOE of the NHES is still comparable to other new forms of electricity generation. It is found that both the NHES and the stand-alone plant are not economical in the existing electricity market. Due to the extra revenue generated, the hybrid system has a positive annual cashflow whereas the stand-alone plant does not. It is found that the hybrid plant needs less cost subsidy than a stand-alone plant to become economical.

08 HYDROGEN↗