Results for “flexible power generation” — Page 44

DOE OSTI2025

HydroForecast Long-term: Improving hydropower’s resilience to climate change through accurate climate-scale

With hydrologic patterns and water availability across the globe shifting due to climate change, advancements in hydrologic prediction systems can help significantly reduce the uncertainties that utilities and water supply entities have in their decision making. Understanding and estimating hydrology at the climate scale is critical for managing water resources under changing climate scenarios. This project focuses on integrating state-of-the-art neural network modeling with downscaled climate projections to deliver the reliable water supply projections decades into the future to meet an urgent need from hydropower operators and water utilities. In this Phase 1 DOE SBIR proposal, we developed and validated a theory-guided neural network model, HydroForecast Long-term, for climate-scale hydrology and implemented the model within existing HydroForecast infrastructure. HydroForecast Long-term combines the most accurate streamflow modeling system with a flexible and scalable data architecture to generate water supply projections out to the year 2100. This report illustrates that we have achieved our four objectives: 1) create a prototype of HydroForecast Long-term, building the neural network prediction model, 2) build an automated data input pipeline that processes large amounts of data from the latest global temperature and precipitation climate models; 3) benchmark the accuracy of the hydrologic model over the recent two decades over a large set of diverse basins, and 4) create a set of output visuals and summary metrics informed by customer feedback that connect the data to critical decision points. This work empowers water users to make data-informed decisions supporting a resilient, renewable-powered grid and water system. The results advance the Department of Energy’s mission by addressing critical gaps in water supply planning under climate change.

13 HYDRO ENERGY↗

NASA NTRSDate not supplied

Space Nuclear Propulsion for Deep Space Science Missions

The use of nuclear thermal propulsion (NTP) 1 and nuclear electric propulsion (NEP) 2 systems on deep space science missions to the outer planets and into the interstellar medium 3 can yield significant spacecraft system and mission performance benefits and improvements relative to the use of conventional chemical propulsion systems. Several recent and ongoing programs are developing the technologies and systems required to realize a near-term deep space nuclear propulsion capability. NTP provides improved propulsion efficiencies compared to chemical propulsion, while also providing substantial thrust. This combination of high thrust and increased specific impulse (I_sp) provides high acceleration and extended thrusting periods, enabling greatly reduced trip-times on certain types of missions compared to various propulsive alternatives. For examples, compared to a baseline mission using chemical propulsion, NTP-powered missions to Jupiter or Uranus could deliver approximately 2.4-3.6 times more payload (in the case of Jupiter, the payload delivery is significantly larger than the Juno spacecraft). In this comparison, the higher end of the payload advantage is obtained when the trip time is held equal for the NTP-powered vehicle and a vehicle using a chemical propulsion departure stage. NTP systems are presently under development by multiple government agencies. NASA’s Space Nuclear Propulsion (SNP) project aims to demonstrate a hydrogen-fed NTP engine at 900 s specific impulse (I_sp) and approximately 10-15 klb_f of thrust. DARPA’s Demonstration Rocket for Agile Cislunar Operations (DRACO) program is targeting a demonstration of an NTP system in the cislunar space between the Earth and the Moon. An appropriately phased development plan that applies the development of the reactor technology for an NTP engine in this performance class and leverages mature, existing liquid rocket component hardware provides a path to a lower cost propulsion system that can be realized on a shorter development schedule. NEP, with high Isp in the 2,000-8,000 s range, can also provide advantages over chemical propulsion, including a much greater payload delivery mass and the flexibility for planners to trade between delivered mass and a wider window of mission trajectory options. Electric propulsion (EP) systems have demonstrated great utility, performing notably on the Dawn mission to enable rendezvous and orbital insertion at two separate bodies, Vesta and Ceres. An NEP-powered vehicle would have a similar capability to visit multiple bodies, loitering at each before moving to the next. A 10 kW_e NEP system provides a power- rich environment on the spacecraft that is simply not possible using present radioisotope power systems, giving mission planners more scientific instrument and communication hardware options. Several programs and projects are presently developing NEP systems and subsystems in the 10 kW_e power range, leveraging past reactor work and recent nuclear power generation risk-reduction demonstration activities such as the Demonstration Using Flattop Fission (DUFF) and the Kilopower Reactor Using Stirling TechnologY (KRUSTY). The goal of the Air Force Research Laboratory’s Joint Energy Technology Supplying On-Orbit Nuclear Power (JETSON) program is an in-space demonstration vehicle that has a 10 kW_e -class fission power source. These past and present efforts can be combined with the ongoing development of 10 kW_e -class electric propulsion systems (notably the NEXT-C ion thruster or the Hall-effect thrusters for Power and Propulsion Element of the Lunar Gateway) to provide a pathway to a low-cost, reliable NEP system for deep space science application.

Kurt A. Polzin↗

NASA NTRSDate not supplied

Space Nuclear Propulsion for Deep Space Science Missions

The use of nuclear thermal propulsion (NTP) 1 and nuclear electric propulsion (NEP) 2 systems on deep space science missions to the outer planets and into the interstellar medium 3 can yield significant spacecraft system and mission performance benefits and improvements relative to the use of conventional chemical propulsion systems. Several recent and ongoing programs are developing the technologies and systems required to realize a near-term deep space nuclear propulsion capability. NTP provides improved propulsion efficiencies compared to chemical propulsion, while also providing substantial thrust. This combination of high thrust and increased specific impulse (I_sp) provides high acceleration and extended thrusting periods, enabling greatly reduced trip-times on certain types of missions compared to various propulsive alternatives. For examples, compared to a baseline mission using chemical propulsion, NTP-powered missions to Jupiter or Uranus could deliver approximately 2.4-3.6 times more payload (in the case of Jupiter, the payload delivery is significantly larger than the Juno spacecraft). In this comparison, the higher end of the payload advantage is obtained when the trip time is held equal for the NTP-powered vehicle and a vehicle using a chemical propulsion departure stage. NTP systems are presently under development by multiple government agencies. NASA’s Space Nuclear Propulsion (SNP) project aims to demonstrate a hydrogen-fed NTP engine at 900 s specific impulse (I_sp) and approximately 10-15 klb_f of thrust. DARPA’s Demonstration Rocket for Agile Cislunar Operations (DRACO) program is targeting a demonstration of an NTP system in the cislunar space between the Earth and the Moon. An appropriately phased development plan that applies the development of the reactor technology for an NTP engine in this performance class and leverages mature, existing liquid rocket component hardware provides a path to a lower cost propulsion system that can be realized on a shorter development schedule. NEP, with high Isp in the 2,000-8,000 s range, can also provide advantages over chemical propulsion, including a much greater payload delivery mass and the flexibility for planners to trade between delivered mass and a wider window of mission trajectory options. Electric propulsion (EP) systems have demonstrated great utility, performing notably on the Dawn mission to enable rendezvous and orbital insertion at two separate bodies, Vesta and Ceres. An NEP-powered vehicle would have a similar capability to visit multiple bodies, loitering at each before moving to the next. A 10 kW_e NEP system provides a power- rich environment on the spacecraft that is simply not possible using present radioisotope power systems, giving mission planners more scientific instrument and communication hardware options. Several programs and projects are presently developing NEP systems and subsystems in the 10 kW_e power range, leveraging past reactor work and recent nuclear power generation risk-reduction demonstration activities such as the Demonstration Using Flattop Fission (DUFF) and the Kilopower Reactor Using Stirling TechnologY (KRUSTY). The goal of the Air Force Research Laboratory’s Joint Energy Technology Supplying On-Orbit Nuclear Power (JETSON) program is an in-space demonstration vehicle that has a 10 kW_e -class fission power source. These past and present efforts can be combined with the ongoing development of 10 kW_e -class electric propulsion systems (notably the NEXT-C ion thruster or the Hall-effect thrusters for Power and Propulsion Element of the Lunar Gateway) to provide a pathway to a low-cost, reliable NEP system for deep space science application.

Kurt A. Polzin↗

DOE OSTI2026

Bounding the costs of electric vehicle managed charging—supply curves for scenarios from 2025 to 2050

As electric vehicle (EV) adoption increases, the resulting EV battery charging will increase demand on the electric power grid. Through EV managed charging (EVMC) programs, charging can be shifted in time to support electric grid reliability and reduce electricity costs. EVMC can offer an alternative to additional supply-side generation, but the costs of EVMC implementation must be understood to evaluate the cost-benefits of EVMC. This paper presents bottom-up, forward-looking (from 2025 through 2050) estimates of the incremental costs associated with different EVMC dispatch mechanisms available to electric utilities. The costs of enabling EVMC for a range of customer participation levels are presented in the form of supply curves, which provide per-EV costs for a targeted level of participation. The largest drivers of cost variation are assumptions about future charging flexibility paradigms described in four scenarios. These supply curves can be used to quantify the expected costs of EVMC programs and enable comparison with supply-side or other demand flexibility alternatives.

25 ENERGY STORAGE↗

DOE OSTI2021

Valuation of Wind Energy Turbines Using Volatility of Wind and Price

The limitedness of the nonrenewable local energy resources in Israel, even in the background of the later gas fields’ findings, continues to force the state to devote various efforts towards ‘green’ energy development. These efforts include installations, both for the solar and for wind energy, thus improving the diversity of energy sources. While the standard discounted cash flow (DCF) method using the net present value (NPV) criterion is extensively adopted to evaluate investments, the standard DCF method is inappropriate for the rapidly changing investment climate and for the managerial flexibility in investment decisions. In recent years, the real options analysis (ROA) technique has been widely applied in many studies for the valuation of renewable energy investment projects. Taking into account the above background, we apply, in this study, the real options analysis approach for the valuation of wind energy turbines and apply it to the analysis of wind energy economic potential in Israel, which is the context of our work. We hypothesize that due to nature of wind energy production uncertainties, the ROA method is better than the alternative. The novelty of this paper includes the following: real world wind statistics of the Merom Golan site in Israel (velocity 3.73 m/s, with a standard deviation of 2.03 m/s), a realistic power generation estimation (power generation of 1205.84 kW with a standard deviation of about 0.5% in annual value which is worth about 1.3 M$ per annum), and an economic model to evaluate the profitability of such a project. We thus discuss the existing challenges of diversifying renewable energy sources in Israel by adding wind installations. Our motivation is to introduce a method which will allow investors and officials to take into account uncertainties when deciding in investing in such wind installations. The outcomes of the paper, which are obtained using the method of Weibull statistics and the Black–Scholes ROA technique, include the result that market price volatility adds to the uncertainties much more than any wind fluctuations, provided that the analysis is integrated over a long enough time.

17 WIND ENERGY↗

NASA NTRS1992

An experimental methodology for a fuzzy set preference model

A flexible fuzzy set preference model first requires approximate methodologies for implementation. Fuzzy sets must be defined for each individual consumer using computer software, requiring a minimum of time and expertise on the part of the consumer. The amount of information needed in defining sets must also be established. The model itself must adapt fully to the subject's choice of attributes (vague or precise), attribute levels, and importance weights. The resulting individual-level model should be fully adapted to each consumer. The methodologies needed to develop this model will be equally useful in a new generation of intelligent systems which interact with ordinary consumers, controlling electronic devices through fuzzy expert systems or making recommendations based on a variety of inputs. The power of personal computers and their acceptance by consumers has yet to be fully utilized to create interactive knowledge systems that fully adapt their function to the user. Understanding individual consumer preferences is critical to the design of new products and the estimation of demand (market share) for existing products, which in turn is an input to management systems concerned with production and distribution. The question of what to make, for whom to make it and how much to make requires an understanding of the customer's preferences and the trade-offs that exist between alternatives. Conjoint analysis is a widely used methodology which de-composes an overall preference for an object into a combination of preferences for its constituent parts (attributes such as taste and price), which are combined using an appropriate combination function. Preferences are often expressed using linguistic terms which cannot be represented in conjoint models. Current models are also not implemented an individual level, making it difficult to reach meaningful conclusions about the cause of an individual's behavior from an aggregate model. The combination of complex aggregate models and vague linguistic preferences has greatly limited the usefulness and predictive validity of existing preference models. A fuzzy set preference model that uses linguistic variables and a fully interactive implementation should be able to simultaneously address these issues and substantially improve the accuracy of demand estimates. The parallel implementation of crisp and fuzzy conjoint models using identical data not only validates the fuzzy set model but also provides an opportunity to assess the impact of fuzzy set definitions and individual attribute choices implemented in the interactive methodology developed in this research. The generalized experimental tools needed for conjoint models can also be applied to many other types of intelligent systems.

Turksen, I. B.↗

NASA NTRS1997

Advanced Free Flight Planner and Dispatcher's Workstation: Preliminary Design Specification

The National Aeronautics and Space Administration (NASA) has implemented the Advanced Air Transportation Technology (AATT) program to investigate future improvements to the national and international air traffic management systems. This research, as part of the AATT program, developed preliminary design requirements for an advanced Airline Operations Control (AOC) dispatcher's workstation, with emphasis on flight planning. This design will support the implementation of an experimental workstation in NASA laboratories that would emulate AOC dispatch operations. The work developed an airline flight plan data base and specified requirements for: a computer tool for generation and evaluation of free flight, user preferred trajectories (UPT); the kernel of an advanced flight planning system to be incorporated into the UPT-generation tool; and an AOC workstation to house the UPT-generation tool and to provide a real-time testing environment. A prototype for the advanced flight plan optimization kernel was developed and demonstrated. The flight planner uses dynamic programming to search a four-dimensional wind and temperature grid to identify the optimal route, altitude and speed for successive segments of a flight. An iterative process is employed in which a series of trajectories are successively refined until the LTPT is identified. The flight planner is designed to function in the current operational environment as well as in free flight. The free flight environment would enable greater flexibility in UPT selection based on alleviation of current procedural constraints. The prototype also takes advantage of advanced computer processing capabilities to implement more powerful optimization routines than would be possible with older computer systems.

Wilson, J.↗

DOE OSTI2023

Privacy-preserving federated learning: Application to behind-the-meter solar photovoltaic generation forecasting

Here, the growing usage of decentralized renewable energy sources has made accurate estimation of their aggregated generation crucial for maintaining grid flexibility and reliability. However, the majority of distributed photovoltaic (PV) systems are behind-the-meter (BTM) and invisible to utilities, leading to three challenges in obtaining an accurate forecast of their aggregated output. Firstly, traditional centralized prediction algorithms used in previous studies may not be appropriate due to privacy concerns. There is therefore a need for decentralized forecasting methods, such as federated learning (FL), to protect privacy. Secondly, there has been no comparison between localized, centralized, and decentralized forecasting methods for BTM PV production, and the trade-off between prediction accuracy and privacy has not been explored. Lastly, the computational time of data-driven prediction algorithms has not been examined. This article presents a FL power forecasting method for PVs, which uses federated learning as a decentralized collaborative modeling approach to train a single model on data from multiple BTM sites. The machine learning network used to design this FL-based BTM PV forecasting model is a multi-layered perceptron, which ensures privacy and security of the data. Comparing the suggested FL forecasting model to non-private centralized and entirely private localized models revealed that it has a high level of accuracy, with an RMSE that is 18.17% lower than localized models and 9.9% higher than centralized models.

14 SOLAR ENERGY↗

DOE OSTI2020

Solar Potential Analysis for the MISO Region

As part of the Minnesota Solar Pathways project, funded in part by the Department of Energy’s Solar Energy Technologies Office, Clean Power Research completed a scenario-based analysis that estimates and optimizes the levelized cost of capacity for high renewables penetrations across the entirety of the Midcontinent Independent System Operator (MISO) region. The analysis concludes that it can be cheaper to overbuild solar + wind and curtail surplus than to store all generation. The analysis described in this report was preceded by an analysis completed in 2018 that used the same modeling framework to optimize generation resources located only in Minnesota.

14 SOLAR ENERGY↗

DOE OSTI2025

Parallel derivative-free optimization for simulation-based design of behind-the-meter energy systems

In this work, the integrated design and dispatch of behind-the-meter or distributed resources (e.g. stationary battery storage and solar PV generation) is considered. A simulation-based framework is employed, generating high-fidelity results with closed-loop predictive control at a fine resolution, at the expense of high computational cost (several minutes to a few hours per design point). To address this challenge, parallel derivative-free design methods are considered. Four methods are compared, including state-of-the-art surrogate-based methods (Radial-Basis Functions and Gaussian processes) and sampling strategies, an evolutionary-based method, and a simple sequential grid refinement method. As a case study, two types of design problem with increasing complexity are considered, namely, the design of behind-the-meter resources (three design variables) and the inclusion of grid capacity (four design variables). The second yields a constrained design problem for which violations can only be determined after solving the computationally expensive simulation. For the three-dimensional case, all methods present a good performance, achieving a solution within 1% of the optimum after the first iteration, with the sequential grid refinement exhibiting the fastest convergence and achieving the best final objective value. This indicates that the parallel evaluation of multiple sampling points may be more important than the choice of method for small decision spaces. For the four-dimensional constrained case, the Genetic Algorithm presents the best tradeoff between performance and computational effort, while the rough objective function terrain generated by constraint violation penalties reduces the performance of surrogate-based methods. Contour plots with flat regions indicate flexibility in the optimal design and highlight the importance of characterizing the solution space.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Next steps in high-repetition-rate laser development for Thomson scattering

Design of a next generation high-rep-rate laser system is underway, aiming for a maximum rep rate of 100 kHz for 1 ms. This will be a "pulse-burst" laser, which is a type of heat-capacity laser. Heat-capacity laser operation is characterized by a burst of pulses of limited duration, with burst length ≤100 ms and pulse rep rate ≥1 kHz. Waste heat accumulates in the laser rod during the burst. This heat is deposited evenly throughout the rod volume, with very little heat removed during the burst, such that temperature rises evenly across the rod radius. Thus beam distortion due to thermal gradients is small. Heat is removed from the rod after the burst, with typically tens of seconds between bursts. Pulse-burst operation of flashlamp pumped Nd:YAG lasers is a cost-effective route to high-rep-rate capability. Pulse-burst laser systems with "fast burst" rep rates in the range of 10–20 kHz have been built for the Thomson scattering diagnostics on MST, NSTX-U, and LHD. For Thomson scattering diagnostic application, the typical requirements are 1064 nm, 1–2 J/pulse, ≤30 ns FWHM pulse, with a top-hat beam profile. A major requirement for this next generation laser system is flexibility in burst sequence programs, ranging from 1 kHz for 100 ms to 100 kHz for 1 ms, and a variety of scenarios in between so that operation can be tailored to plasma experiment requirements. Flashlamp pumping will be used for this next generation laser because it is inexpensive and flexible. A new switch-regulated flashlamp driver will provide improved flashlamp control at lower cost. Additional design issues such as the optimum flashlamp pumping spectrum and optimum Nd doping will be addressed. The use of commercial off-the-shelf components will be maximized in this laser system so that pulse-burst systems can be developed and built by others for new applications.

47 OTHER INSTRUMENTATION↗

DOE OSTI2024

Bulk Power System Flow Control

On July 28, 2023, the Federal Energy Regulatory Commission (FERC) issued Order Number 2023, targeting an improved generation-interconnection process in the United States. FERC Order No. 2023 requires transmission providers to evaluate transmission technologies, other than network expansion, that could provide system improvements and help expand bulk-power system capacity. Transmission providers must consider technologies that would fall under the category of Grid Enhancing Technologies (GETs), which provide capacity-expansion options other than conventional transmission expansion.

32 - ENERGY CONSERVATION, CONSUMPTION, AND UTILIZA↗

DOE OSTI2021

STOCHASTIC OPTIMAL POWER FLOW FOR REAL-TIME MANAGEMENT OF DISTRIBUTED RENEWABLE GENERATION AND DEMAND RESPONSE (Final Report)

To meet the grand challenge of a sustainable energy future, there has been a surge of interest in renewable energy. Today, the uncertainty associated with renewable resources is handled by using operating reserves. The high penetration of renewable resources, however, introduces difficult-to-control dynamics and challenges for power system operation. Decision support tools are necessary at the bulk system operational level to recognize and efficiently utilize renewable resources and distributed demand response products in concert with traditional grid resources. It is envisaged that responsive load can potentially have very significant cost advantages over either spinning or non-spinning ramping reserve. Critical decisions are made during hour(s)-ahead and real-time power system operation regarding the commitment and dispatch of generators to ensure power delivery is both reliable and economic. These decisions are typically made by a security constrained optimal flow, which determines future generator commitments, dispatches, and ensures adequate reserves are available in the event of a contingency (unexpected outage) or if future system conditions deviate from forecasts. However, security has been always based on a pre-specified subset of contingency constraints whose enforcement does not guarantee security under all possible future possibilities while also giving little or no weight to the likelihood of each contingent event or the severity of its consequences. Existing tools, which are based exclusively on deterministic optimization models, do not yield optimal operational decisions to address these new challenges, in terms of both reliability and cost-effectiveness. This project has focused on developing a stochastic optimal power flow (SOPF) framework, which integrates renewable resource uncertainty, load uncertainty, distributed storage (DS), demand response (DR) products, in a holistic manner to address the uncertainty associated with ever-increasing renewable resources, along with the inclusion of distributed demand response products in future power systems. A proof-of-concept problem was created using the Pennsylvania-Jersey-Maryland (PJM) power system network. Synthetic wind generation was added to the system to simulate 50% wind penetration. A 1-hour test of SOPF operation indicated more than 6% operational cost savings. The project continued by adding the Midwestern Independent System Operator (MISO) as a partner, with focus shifting from SOPF to Stochastic Look-Ahead Unit Commitment (SLAC). Unlike PJM, MISO is faced with significant renewable energy resources within its footprint and is challenged with substantial uncertainty in its operations. The SLAC distinguishes itself from existing tools that operators use. At best, today’s tools solve two to three cases independently, where one or two system parameters, such as forecasted load level (e.g., a low, base, and high forecast), are varied and the resulting scenarios are analyzed independently. The stochastic-based optimization of SLAC leverages statistical information from an ensemble of potential operational scenarios and their respective likelihood. The SLAC output can be translated into valuable information to the operator such as suggested commitments, optimal scheduling and dispatch of resources, reserve requirements at both locational and zonal resolutions, ramping availability and requirements, availability of demand response including operational guidance concerning the near-term and real-time coordination between distributed energy resources, and utilization of distributed storage resources. The developed SOPF/SLAC tool, a stand-alone tool compatible with existing EMSs, will provide system operators with unprecedented visibility, flexibility and predictability to these resources and operational guidance concerning the real-time coordination between DERs and DR/DS products. The game changing and practical impact of this disruptive technology will be dramatic and will usher in a new era in the electric power industry, wherein green energy concepts are fully embraced, and electric power costs are lowered throughout the nation.

42 ENGINEERING↗

NASA NTRS1989

Advanced manned space flight simulation and training: An investigation of simulation host computer system concepts

The findings of a preliminary investigation by Southwest Research Institute (SwRI) in simulation host computer concepts is presented. It is designed to aid NASA in evaluating simulation technologies for use in spaceflight training. The focus of the investigation is on the next generation of space simulation systems that will be utilized in training personnel for Space Station Freedom operations. SwRI concludes that NASA should pursue a distributed simulation host computer system architecture for the Space Station Training Facility (SSTF) rather than a centralized mainframe based arrangement. A distributed system offers many advantages and is seen by SwRI as the only architecture that will allow NASA to achieve established functional goals and operational objectives over the life of the Space Station Freedom program. Several distributed, parallel computing systems are available today that offer real-time capabilities for time critical, man-in-the-loop simulation. These systems are flexible in terms of connectivity and configurability, and are easily scaled to meet increasing demands for more computing power.

Montag, Bruce C.↗

NASA NTRS1991

Structured background grids for generation of unstructured grids by advancing front method

A new method of background grid construction is introduced for generation of unstructured tetrahedral grids using the advancing-front technique. Unlike the conventional triangular/tetrahedral background grids which are difficult to construct and usually inadequate in performance, the new method exploits the simplicity of uniform Cartesian meshes and provides grids of better quality. The approach is analogous to solving a steady-state heat conduction problem with discrete heat sources. The spacing parameters of grid points are distributed over the nodes of a Cartesian background grid by interpolating from a few prescribed sources and solving a Poisson equation. To increase the control over the grid point distribution, a directional clustering approach is used. The new method is convenient to use and provides better grid quality and flexibility. Sample results are presented to demonstrate the power of the method.

Pirzadeh, Shahyar↗

NASA NTRS2017

Engineering of Methane Metabolism in Pichia Pastoris Through Methane Monooxygenase Expression

Exploration of the solar system is constrained by the cost of moving mass off Earth. Producing materials in situ will reduce the mass that must be delivered from earth. CO2 is abundant on Mars and manned spacecraft. On the ISS, NASA reacts excess CO2 with H2 to generate CH4 and H2O using the Sabatier System. The resulting water is recovered into the ISS, but the methane is vented to space. Thus, there is a capability need for systems that convert methane into valuable materials. Methanotrophic bacteria consume methane but these are poor synthetic biology platforms. Thus, there is a knowledge gap in utilizing methane in a robust and flexible synthetic biology platform. The yeast Pichia pastoris is a refined microbial factory that is used widely by industry because it efficiently secretes products. Pichia could produce a variety of useful products in space. Pichia does not consume methane but robustly consumes methanol, which is one enzymatic step removed from methane. Our goal is to engineer Pichia to consume methane thereby creating a powerful methane-consuming microbial factory.

Pichia↗

NASA NTRS2017

New Frontiers in Synthetic Biology for Spaceflight

Exploration of the solar system is constrained by the cost of moving mass off Earth. Producing materials in situ will reduce the mass that must be delivered from earth. CO2 is abundant on Mars and manned spacecraft. On the ISS, NASA reacts excess CO2 with H2 to generate CH4 and H2O using the Sabatier System. The resulting water is recovered into the ISS, but the methane is vented to space. Thus, there is a capability need for systems that convert methane into valuable materials. Methanotrophic bacteria consume methane but these are poor synthetic biology platforms. Thus, there is a knowledge gap in utilizing methane in a robust and flexible synthetic biology platform. The yeast Pichia pastoris is a refined microbial factory that is used widely by industry because it efficiently secretes products. Pichia could produce a variety of useful products in space. Pichia does not consume methane but robustly consumes methanol, which is one enzymatic step removed from methane. Our goal is to engineer Pichia to consume methane thereby creating a powerful methane-consuming microbial factory.

Galazka, Jonathan M.↗

NASA NTRS2017

Methane Metabolism by Yeast for Solar System Exploration

Exploration of the solar system is constrained by the cost of moving mass off Earth. Producing materials in situ will reduce the mass that must be delivered from earth. CO2 is abundant on Mars and manned spacecraft. On the ISS, NASA reacts excess CO2 with H2 to generate CH4 and H2O using the Sabatier System. The resulting water is recovered into the ISS, but the methane is vented to space. Thus, there is a capability need for systems that convert methane into valuable materials. Methanotrophic bacteria consume methane but these are poor synthetic biology platforms. Thus, there is a knowledge gap in utilizing methane in a robust and flexible synthetic biology platform. The yeast Pichia pastoris is a refined microbial factory that is used widely by industry because it efficiently secretes products. Pichia could produce a variety of useful products in space. Pichia does not consume methane but robustly consumes methanol, which is one enzymatic step removed from methane. Our goal is to engineer Pichia to consume methane thereby creating a powerful methane-consuming microbial factory.

Galazka, Jonathan M.↗