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Guittet, Darice

Publications and source records attributed to Guittet, Darice.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Beyond Price-Taker: Multiscale Optimization of a Wind-Battery Integrated Energy System within the Wholesale Electricity Market

This work presents the optimization of a wind-battery IES using the multiscale optimization framework proposed in our previous work to quantify errors from the price-taker assumption. The framework, built over Prescient (an open-source package for solving production cost models), is applied to the RTS-GMLC dataset, an open-source dataset that is representative of the southwest U.S. wholesale electricity market. The framework provides detailed bidding, market clearing, and control processes of an IES, and it can quantify how the IES interacts with the market. In this work, we use the retrofit of a wind farm with a battery storage system as an example to show the difference in the market outcomes and revenues obtained from both price-taker and multiscale optimization approaches. Our work goes beyond price-taker and deep dives into quantifying IES-market interaction in optimizing IES. This framework enables users to explore how different design and operation decisions of energy systems interact with the market and provides a more accurate evaluation than the price-taker assumption.

Chen, Xinhe↗
DOE OSTI2024

Conceptual Design of Integrated Energy Systems with Market Interaction Surrogate Models

Most integrated energy system (IES) optimization frameworks employ the price-taker approximation, which ignores important interactions with market and can result in overestimated economic values. In this work, we pro-pose a machine learning surrogate-assisted optimization framework to quantify the IES/market interactions and thus go beyond price taker. We use time series clustering to generate representative IES operation profiles for the IES optimization problem and use machine learning surrogate models to predict the IES/market interaction. We quantify the accuracy of the time series clustering and surrogate models in a case study to optimally retrofit a nuclear power plant with polymer electrolyte membrane electrolyzer to co-produce electricity and hydrogen.

Chen, Xinhe↗
DOE OSTI2024

Conceptual Design of Integrated Energy Systems with Market Interaction Surrogate Models

Most integrated energy system (IES) optimization frameworks employ the price-taker approximation, which ignores important interactions with the market and can result in overestimated economic values. In this work, we propose a machine learning surrogate-assisted optimization framework to quantify IES/market interactions and thus go beyond price-taker. We use time series clustering to generate representative IES operation profiles for the optimization problem and use machine learning surrogate models to predict the IES/market interaction. We quantify the accuracy of the time series clustering and surrogate models in a case study to optimally retrofit a nuclear power plant with a polymer electrolyte membrane electrolyzer to co-produce electricity and hydrogen.

Chen, Xinhe↗
DOE OSTI2024

Powered by PRECISE [Slides]

This presentation is part of the National Renewable Energy Laboratory's (NREL's) Powered By webinar series, hosted by the Grid Planning and Analysis Center. The series highlights NREL's impressive suite of grid planning and analysis tools to help attendees better understand their capabilities and applications. In this presentation, NREL power grid researchers, Killian McKenna and Darice Guittet, and the principal electrical distribution engineer at Sacramento Municipal Utility District, Sheikh Hassan, discuss NREL's PRECISE interconnection tool. PRECISE automates holistic distributed energy resource interconnection studies for distribution utilities by leveraging diverse utility datasets.

14 SOLAR ENERGY↗
DOE OSTI2024

Levelized cost of charging of extreme fast charging with stationary LMO/LTO batteries

Extreme DC fast charging for electric vehicles (EVs) could be competitive with the internal combustion engine refueling experience and enable longer-distance travel, which could help with EV adoption and decarbonization, but these systems have high capital costs and extremely variable high-power demands. Behind-the-meter systems (BTMS) could support extreme-fast-charging (XFC) stations to increase nationwide adoption of EVs. Here, this study examines the optimal break-even levelized cost of charging (LCOC) across 96 BTMS scenarios to enable low-wait XFC stations providing 200 miles of charge in 10 min. This research simulates LCOC via synthetic XFC-capable EV loads, machine-learned battery life models from testing data, and nonlinear optimal controls, co-minimizing complex utility costs and battery replacements. An aggregate optimal BTMS design treating each EV load as equal likely gives an optimal LCOC per utility rate, the average of which is $\$$0.59/kWh. In addition, the sensitivity of optimal and off-optimal design factors, the long-life LMO/LTO chemistry, and optimized controls are analyzed. The battery control model, based on battery stressors to compare chemistries, optimizes LMO/LTO resting state of charge and cycle depth without compromising cost reduction, which enables greater flexibility in operation. The LCOC savings due to replacement reduction are small, up to $\$$0.035/kWh (6%), with an average of $\$$0.02/kWh (3.5%). Compared with gasoline stations, the aggregate XFC station design achieves comparable speed, experience of service, and cost at $\$$3.81/gal gasoline, showing that EVs can replace gasoline vehicles even for longer-distance travel.

25 ENERGY STORAGE↗
DOE OSTI2023

Conceptual Design of Integrated Energy Systems via Multiscale Market Simulations and Surrogate Models for Market Interactions

This final report describes novel capabilities developed as part of DISPATCHES, Design Integration and Synthesis Platform to Advance Tightly Coupled Hybrid Energy Systems, for designing hybrid energy systems (HES) a.k.a. integrated energy systems (IES) in the context of a larger electricity market. Capabilities are demonstrated on case studies for nuclear and renewable power.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Beyond Price Taker: Conceptual Design and Optimization of Integrated Energy Systems Using Machine Learning Market Surrogates

Future electricity generation systems must be optimized to provide flexibility that counteracts the variability of non-dispatchable renewable energy sources and ensures the reliability and safety of critical infrastructure, including the electric grid. The current state-of-the-art is to co-optimize the design and operation of integrated energy systems (IES) treating historical or predicted time-series electricity prices as fixed parameters. Recent literature has shown the limitations of this price taker assumption, which neglects how IES optimization decisions influence market outcomes. As such, this paper proposes a new optimization formulation that uses machine learning surrogate models, trained from a library of annual market operation simulations, to embed IES market interactions into the co-optimization problem directly. Using a thermal generator example built in the open-source IDAES computational environment, we show that the price taker approach routinely over-predicts annual revenues by 8% or more compared to a validation simulation, where the proposed approach has a typical relative error of 1% or less.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Market-Integrated Optimization of Wind-Battery-Hydrogen Hybrids for Peaking Capacity via Storage

As Integrated Energy Systems (IES) combine multiple energy and storage technologies to provide potentially more value and less risk via resource diversification, complementary overbuild, increased flexibility, and revenue-stacking, IES value is dependent on electricity market dispatch and grid interactions should play an important role in IES design and operation. This study hybridizes and retrofits wind and combustion turbine plants to study the impacts of replacing gas generation capacity with wind, battery, PEM electrolysis, hydrogen tanks and hydrogen turbines. The optimized design is co-simulated in a production cost model with different bidding strategies in order to compare performance and highlight the importance of grid-interactions. We analyze the revenue and dispatch changes as well as the price and cost implications of wind-battery-hydrogen IESs.

electrolysis↗
DOE OSTI2022

HOPP - Hybrid Optimization and Performance Platform

The Hybrid Optimization and Performance Platform, HOPP, is a wind + solar + battery + X design software for optimizing co-located, utility-scale hybrid plants down to the component level for different markets and technoeconomic objectives. Key technology and financial inputs to the HOPP model that inform the objective to be optimized are presented. The layout and performance integration is combined with optimal dispatch and full financial modeling within an optimization framework. With an example scenario, optimal sizing and layout results are shown in a sensitivity analysis of prices for two hybrid configurations.

batteries↗
DOE OSTI2022

A simplified, efficient approach to hybrid wind and solar plant site optimization

Abstract. Wind plant layout optimization is a difficult, complex problem with a large number of variables and many local minima. Layout optimization only becomes more difficult with the addition of solar generation. In this paper, we propose a parameterized approach to wind and solar hybrid power plant layout optimization that greatly reduces problem dimensionality while guaranteeing that the generated layouts have a desirable regular structure. Thus far, hybrid power plant optimization research has focused on system sizing. We go beyond sizing and present a practical approach to optimizing the physical layout of a wind–solar hybrid power plant. We argue that the evolution strategy class of derivative-free optimization methods is well-suited to the parameterized hybrid layout problem, and we demonstrate how hard layout constraints (e.g., placement restrictions) can be transformed into soft constraints that are amenable to optimization using evolution strategies. Next, we present experimental results on four test sites, demonstrating the viability, reliability, and effectiveness of the parameterized evolution strategy approach for generating optimized hybrid plant layouts. Completing the tool kit for parameterized layout generation, we include a brief tutorial describing how the parameterized evolutionary approach can be inspected, understood, and debugged when applied to hybrid plant layouts.

14 SOLAR ENERGY↗
DOE OSTI2021

Heuristic Dispatch Based on Price Signals for Behind-the-Meter PV-Battery Systems in the System Advisor Model

The economic potential of a behind-the-meter (BTM) PV-battery system depends greatly on how the battery is dispatched. Different utility rates, system sizes, generation and load profiles can all require different dispatch strategies. This paper presents price signals dispatch, a new algorithm for automated economic dispatch of BTM PV-battery systems, which utilizes 24-hour PV and load forecasts, degradation data, and utility rates. The algorithm is integrated with the System Advisor Model (SAM) tool and is tested with a nonlinear generic electrochemical battery model. Price signals dispatch outperforms SAM's existing algorithms in cases requiring a balance between demand charge management and energy arbitrage, and in cases where battery degradation imposes a significant cost.

batteries↗
DOE OSTI2021

Design, Integration and Synthesis Platform to Advance Tightly Coupled Hybrid Energy Systems (DISPATCHES) v0.1.0

The DISPATCHES software will enable the design and optimization of Integrated Energy Systems (IES). The platform will support hybridizing existing generation facilities (such as nuclear and coal plants) as well as greenfield hybrid designs that incorporate advanced technologies (such as Advanced Modular Reactors and Coal FIRST), and it will enable the consideration of multiple technology options with different time constants. The design of the IES will be optimized for operation within the bulk power system via energy market signals (day ahead, real-time, and ancillary services) obtained from a production cost model. The goal of DISPATCHES is to identify flexible, dynamic designs that are responsive to grid market signals resulting in highly efficient, cost effective tightly coupled hybrid energy systems that can act as a "black box" to the grid to provide increased flexibility and resiliency.

Gunter, Daniel↗
DOE OSTI2021

Foundational Open Source Solar System Modeling Through Improvement and Validation of the System Advisor Model and PVWatts (FY19-FY21 Final Technical Report)

Accelerating intelligent deployment of solar energy technologies demands accurate system modeling every step of the way. From project development to policy research, grid integration studies to development of novel technologies, industry and researchers alike need validated, transparent, easy-to-use, extensible, cutting-edge, and accurate models of both the performance and financing of solar systems. The System Advisor Model (SAM) and PVWatts tools provide a platform to fill that need. The overarching goal of this set of software tools is to enable accurate PV system modeling across the industry, and our usage metrics indicate that we continue to succeed in that endeavor, with a user starting SAM every 2 minutes globally, and over 17 million PVWatts hits per month. This project leveraged DOEs past investment in the SAM and PVWatts platforms to continue to provide valuable and extensible PV, battery, and financial modeling resources to the larger solar community. We pursued multiple avenues in parallel: software maintenance and technical support that are foundational to the continued usability of the SAM and PVWatts platforms; platform and PV model improvements and stakeholder engagement activities that are core to the continued relevance of the platforms; and open source activities to foster the continued creation of a vibrant open-source community around the SAM and PVWatts tools, which opens up exciting new opportunities for industry interaction.

14 SOLAR ENERGY↗
DOE OSTI2019

Polyacrylonitrile Interactions with Carbon Nanotubes in Solution: Conformations and Binding as a Function of Solvent, Temperature, and Concentration

Abstract Polyacrylonitrile (PAN) is among the most promising precursor polymers to produce strong and lightweight carbon fiber. Conformations in solution and the extent of binding to carbon nanotubes (CNTs) are critical during gel spinning and for alignment of graphitic layers upon carbonization. Here, quantitative insights into these processes are reported using molecular dynamics simulations at the atomic scale including virtual π electrons and comparisons to experimental data. Common solvents for fiber spinning induce significant differences in PAN conformation in dilute solutions at 25 °C with persistence lengths between 0.5 and 2 nm. Variations in conformation become smaller at 75 °C, in the presence of CNTs, and at higher PAN concentration. “Aging” of PAN conformations in dimethylformamide and dimethylsulfoxide at higher temperature is explained and a correlation between extended polymer conformations and increased binding to CNTs is identified in dilute solutions. PAN is overall barely attracted to CNTs under common solution conditions and enters significant surface contact only at higher concentration as solvent is physically removed. The impact of temperature is small, whereby binding increases at lower temperatures. The results provide guidance to control interactions of polymers with CNTs to induce distinct conformations and specific binding at the early stages of assembly.

Pramanik, Chandrani↗
DOE OSTI2019

PySAM (Python Wrapper for System Advisor Model "SAM") [SWR-19-57]

PySAM is a Python wrapper for the National Renewable Energy Laboratory’s System Advisor Model (SAM). SAM is a performance and financial model designed to facilitate decision making for people involved in the renewable energy industry. PySAM provides a native Python interface for the models found in SAM Simulation Core, SSC.

Gilman, Paul↗