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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 181 records · Page 10

Environmental and economic optima of solar home systems design: A combined LCA and LCC approach

Here, we compare the economic and environmental optimal design of Solar Home Systems (SHSs) and explore the role of economic incentives (such as tariffs and technology costs) in approximating the two optima. To achieve that, we present a methodology for the environmental and economic evaluation of grid-connected SHSs: user-scale electric systems involving a photovoltaic (PV) power system and a battery energy storage system. The proposed methodology is based on a mixed integer linear programming (MILP) optimization, life cycle assessment and life cycle costing. This methodological framework is applied to a case study involving a typical SHS installation in Italy. The results of the environmental optimal design brought to the evaluation of a 3.25 kW PV assisted by 8.66 kWh of nickel cobalt manganese batteries, whereas the costs of the SHS are minimized by a small PV system (less than 1 kW). Results underline that the environmental optimal configurations rely on battery technologies, which entails a significant cost compared to the grid connection. In contrast, the economic optimal design solutions is less impactful than the grid mix both from an environmental and economic points of view. Thanks to a reduction of batteries and PV costs, the environmental impact of the economic optimal design is expected to decrease in the future.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

A scalable planning framework of energy storage systems under frequency dynamics constraints

As the penetration of renewables increases in power systems, the declining system inertia can cause frequency stability issues. Battery energy storage systems (BESSs) respond fast and therefore can relieve the low inertia difficulty but need to be appropriately sized considering the associated cost. This paper presents a novel stochastic optimization model for economically planning BESS capacity while considering the spatial–temporal correlation of wind generation and generator outages under frequency stability constraints, which include the rate-of-change of frequency (RoCoF), frequency nadir (FN), and quasi-steady-state (QSS) frequency. A set of new FN constraints that can be easily linearized is developed. To account for renewable uncertainties, a realistic uncertainty modeling approach, Random Field, is adopted to generate wind generation scenarios by considering both spatial and temporal evolutions of wind speed profiles. The ESS sizing is formulated as a mixed-integer linear programming problem and solved by using a scalable decomposition-and-coordination approach, Surrogate Absolute Value Lagrangian Relaxation (SAVLR). To further improve the scalability and reduce computational burdens, a rolling-horizon-based update is developed and incorporated into SAVLR for providing a practical solution to the long-term planning of very large-scale power systems. Finally, a modified IEEE 118-bus system and the Polish system are used to validate the effectiveness and scalability of the model and solution methodology.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Solar Energy Implementation Strategies on Picuris Pueblo

Picuris Pueblo is a small tribal community in Northern New Mexico consisting of about 306 members and 86 homes. Picuris Pueblo has made advances with renewable energy implementation, including the installation of a 1 megawatt photovoltaic (PV) array. This array has provided the tribe with economic and other benefits that contribute toward the tribe's goal of tribal sovereignty. The tribe is seeking to implement more PV generation as well as battery energy storage systems. Picuris Pueblo is considering different implementation methods, including the formation of a microgrid system. This report studies the potential implementation of a PV and battery storage microgrid system and the associated benefits and challenges. The benefits of a microgrid system include cost savings, increased resiliency, and increased tribal sovereignty and align with the tribe's goals of becoming energy independent and lowering the cost of electricity.

14 SOLAR ENERGY↗

Distributed Wind-Hybrid Microgrids with Autonomous Controls and Forecasting

Distributed wind-hybrid microgrids have the potential to provide key resilience and economic benefits to both the customers they serve and the utility grids they are connected to. Such microgrids will likely be a key part of the grid of the future, whether connected to large utility grids or linked together in multi-microgrid systems. Through the hybridization of distributed wind and solar photovoltaics, autonomous device-level and system-level controls, battery energy storage systems with smart inverters, and forecasting, these microgrids could maintain local stability and provide grid services - all with renewable power. In the literature, these elements have been considered individually. However, they have not been combined and demonstrated at a high fidelity, which is essential to prove the concept's operation before moving to hardware-in-the-loop and physical demonstrations. In this work, we develop a high-fidelity MATLAB-Simulink model of a real distributed wind-hybrid microgrid that includes all these elements. We demonstrate the microgrid maintaining stability and production in a variety of islanded, grid-connected, and transition scenarios. This includes riding through faults and grid transitions, handling resource variability, and providing grid services. The results demonstrate, at a high fidelity, how distributed wind-hybrid microgrids can operate in an economic and resilient fashion. Finally, we provide recommendations for future research to move advanced distributed wind-hybrid microgrids toward deployment.

ancillary services↗

The Effect of Clean Energy Generation Targets on the Portfolio of Electric Grid Generation Technologies

A least-cost generation and transmission expansion planning model is used to optimize the U.S. generation portfolio in 2030 under a range of Clean Energy Generation Target (CEGT) policy goals. As reference cases for comparison, the model is used to optimize the generation portfolio for the future electric grid with and without the Investment and Production Tax Credits (ITC and PTC). Next, the model is used to optimize the generation portfolio with a CEGT ranging from 10% to 70%. The results show that due to the ITC and PTC extensions by the 2022 Inflation Reduction Act, there is little additional investment in renewable energy generation assets at low CEGT levels. Significant additional installations of both renewable energy generation and energy storage systems – both batteries and pumped storage hydro – become important at CEGT levels above approximately 30%.

Aldeman, Matthew↗

An innovative heterogeneous transfer learning framework to enhance the scalability of deep reinforcement learning controllers in buildings with integrated energy systems

Deep Reinforcement Learning (DRL)-based control shows enhanced performance in the management of integrated energy systems when compared with Rule-Based Controllers (RBCs), but it still lacks scalability and generalisation due to the necessity of using tailored models for the training process. Transfer Learning (TL) is a potential solution to address this limitation. However, existing TL applications in building control have been mostly tested among buildings with similar features, not addressing the need to scale up advanced control in real-world scenarios with diverse energy systems. This paper assesses the performance of an online heterogeneous TL strategy, comparing it with RBC and offline and online DRL controllers in a simulation setup using EnergyPlus and Python. The study tests the transfer in both transductive and inductive settings of a DRL policy designed to manage a chiller coupled with a Thermal Energy Storage (TES). The control policy is pre-trained on a source building and transferred to various target buildings characterised by an integrated energy system including photovoltaic and battery energy storage systems, different building envelope features, occupancy schedule and boundary conditions (e.g., weather and price signal). The TL approach incorporates model slicing, imitation learning and fine-tuning to handle diverse state spaces and reward functions between source and target buildings. Results show that the proposed methodology leads to a reduction of 10% in electricity cost and between 10% and 40% in the mean value of the daily average temperature violation rate compared to RBC and online DRL controllers. Moreover, online TL maximises self-sufficiency and self-consumption by 9% and 11% with respect to RBC. Conversely, online TL achieves worse performance compared to offline DRL in either transductive or inductive settings. However, offline Deep Reinforcement Learning (DRL) agents should be trained at least for 15 episodes to reach the same level of performance as the online TL. Therefore, the proposed online TL methodology is effective, completely model-free and it can be directly implemented in real buildings with satisfying performance.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Electric-vehicle battery second-life and recycling pathways: How economics depend on chemistry, processing, and application

We assess the economics of repurposing and recycling electric vehicle (EV) batteries by estimating the maximum acquisition price repurposers and recyclers could pay for used EV packs across cathode chemistries, first-life conditions, second-life applications, and recycling processes. We develop a novel open-source process-based cost model of a UL-1974-certified repurposing facility and leverage battery degradation models to estimate the maximum acquisition price repurposers could pay for used EV batteries while producing second-life battery energy storage systems with life-adjusted costs equivalent to new systems. We compare these maximum price estimates to maximum prices for recyclers based on cost and revenue estimates from the EverBatt model. We find that repurposing is more economical than recycling for lithium iron phosphate (LFP) batteries, due to their relatively long life and low value materials; recycling is generally more economical than repurposing for lithium nickel cobalt aluminum oxide (NCA) batteries, due to their shorter life and higher value materials; and the economics for lithium nickel manganese cobalt oxide (NMC) batteries depend more heavily on first life retirement conditions and second life application intensity. These results suggest an overall strategy: reuse LFP, recycle NCA, and sort NMC into recycling or repurposing pathways based on state of health and second-life application.

25 ENERGY STORAGE↗

Arbitrage and Capacity Firming in Coordination with Day-Ahead Bidding of a Hybrid PV Plant

A hybrid PV plant (HPP) combines a photovoltaic (PV) plant with a battery energy storage system (BESS), which is considered a promising step towards the future of renewable power plants by the U.S. Department of Energy. When the renewable penetration reaches a significant level, a hybrid PV plant can bid in as a controllable thermal plant in the future electricity market. In this study, a bidding and BESS scheduling model is proposed for the HPP. The robust optimization (RO) technique has been utilized to identify the worst-case scenario of uncertainties during the bidding process. To address the overly conservative issue of the single-stage RO, we have decoupled the BESS schedule for arbitrage and PV capacity firming by a two-stage RO formulation. By comparing the output of single-stage RO and two-stage RO, the two-stage RO bids and schedules in a more aggressive manner, which increases the income of HPP. Also, the penalty of under-generation is considered in our model so that the day-ahead bidding decision and arbitrage schedules can be adjusted based on the potential UNDER-GENERATION penalty. Because the proposed model is non-convex and contains multi-stages, the Column-and-Constraint Generation (C&CG) algorithm is applied to the model as the solution. The proposed model has shown better economic performance compared to a state-of-art single-stage bidding method in case studies.

BESS scheduling↗

Anomaly Identification of Synchronized Voltage Waveform for Situational Awareness of Low Inertia Systems

Inverter-based resources (IBRs) such as photovoltaics (PVs), wind turbines, and battery energy storage systems (BESSs) are widely deployed in low-carbon power systems. However, these resources typically do not provide the inertia needed for grid stability, resulting in a low-inertia power system. IBRs and lack of inertia have been known to cause anomalies such as waveform distortions and wideband oscillations in power systems due to the limited inertia level, leading to increased generation trips and load shedding. Here, to achieve effective anomaly identification, this paper proposes a synchro-waveform-based algorithm utilizing real-time synchronized voltage waveform measurements from waveform measurement units (WMUs). In the proposed method, different physical characteristics, as well as statistical features, are extracted from synchronized voltage waveform measurements to filter anomalies. Then, the anomaly identification approach based on the random forest is developed and deployed into the FNET/GridEye system considering trade-offs among accuracy, computational burden, and deployment cost. Moreover, four WMUs are specially designed and deployed on Kauai Island to receive instantaneous synchronized voltage waveform measurements. To verify the performance of the proposed algorithm, different experiments are carried out with collected field test data. The result demonstrates that the performance of the proposed synchro-waveform-based anomaly categorization algorithm can accurately identify anomalies 95.35% of the time, which has comparable performance among benchmarking algorithms.

Situational awareness↗

Techno-economic analysis of renewable energy generation at the South Pole

Transitioning from fossil-fuel power generation to renewable energy generation and energy storage in remote locations has the potential to reduce both carbon emissions and cost. Here, this study presents a techno-economic analysis for implementation of a hybrid renewable energy system at the South Pole in Antarctica, which currently hosts several high-energy physics experiments with nontrivial power needs. A tailored model of resource availability and economics for solar photovoltaics, wind turbine generators, lithium-ion energy storage, and long-duration energy storage at this site is explored in different combinations with and without existing diesel energy generation. The Renewable Energy Integration and Optimization (REopt) platform is used to determine the optimal system component sizing and the associated system economics and environmental benefit. We find that the least-cost system includes all three energy generation sources and lithium-ion energy storage. For an example steady-state load of 170 kW, this hybrid system includes 180 kW-DC of photovoltaic panels, 570 kW of wind turbines, and a 3.4 MWh lithium-ion battery energy storage system. This system reduces diesel consumption by 95% compared to an all -diesel configuration, resulting in approximately 1200 metric tons of carbon footprint avoided annually. Over the course of a 15-year analysis period the reduced diesel usage leads to a net savings of 57 million United States dollars, with a time to payback of approximately two years. All the scenarios modeled show that the transition to renewables is highly cost effective under the unique economics and constraints of this extremely remote site.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Paired Hydro-Battery Hybrid System Operations Using MIQP Based Multi-Objective Optimization

Hybridizing a hydropower plant with a battery energy storage system is often a very expensive decision. So to make the case for feasibility of hybridization the cost benefit analysis must be comprehensive. Most literature found on this subject only uses one out of many different available value streams to carry out a feasibility analysis. To that end, in this study we propose a multi-objective optimization formulation and a mixed-integer quadratic programming optimization engine that considers multiple different value streams and optimizes hydro-battery hybrid system paired operations to maximize revenue generated from energy arbitrage while simultaneously minimizing the total hydro turbine mileage thus reducing turbine starts and stops and improving turbines life all while following all environmental constraints and not violating any limitations either regulatory or preferential (i.e., to support recreational activities like white water rafting, etc.). In this study we also implemented the developed optimization methodology to a real-world case study of Bagnell dam hydropower facility (8 units totaling 240 MW rated power capacity) which is owned and operated by our industry partner Ameren Energy Inc. The case study outcomes shows that by hybridizing the Bagnell dam hydropower facility with a 60 MW x 2-hr battery energy storage system, the annual benefits can be increased over \$6 million while reducing the annual mileage averaged per turbine and number of start/stops by over 98\% and 85\% respectively.

Chalishazar, Vishvas H.↗

Predictive Battery Lifetime Modeling at NREL [Slides]

Battery lifetime models are used to extrapolate data from accelerated aging tests to simulate degradation in real-world applications such as electric vehicles and battery energy storage systems. Methods developed at NREL utilize both expert domain-knowledge and machine-learning to identify models, using statistical methods such as cross-validation and bootstrap resampling to interrogate model performance and quantify uncertainty. These models can be utilized in systems level simulations to predict battery performance or technoeconomic models to estimate the lifetime cost of battery systems.

25 ENERGY STORAGE↗

Review of Wave Energy Converter Power Take-Off Systems, Testing Practices, and Evaluation Metrics: Preprint

While the field of wave energy has been the subject of numerical simulation, scale model testing, and precommercial project testing for decades, wave energy technologies remain in the early stages of development and must continuing proving themselves as a promising modern renewable energy field. One of the difficulties that wave energy systems have been struggling to overcome is the design of highly efficient energy conversion systems that can convert the mechanical power, derived from the oscillation of wave activated bodies, into another useful product. Often the power take-off (PTO) is defined as the single unit responsible for converting mechanical power into another usable form such as electricity, pressurized fluid, compressed air, and others. The PTO, and the entire power conversion chain (PCC), is of great importance as it affects not only how efficient wave power is converted into electricity, but also contributes to the mass, size, structural dynamics, and levelized cost of energy (LCOE) of the wave energy converter (WEC). Unlike wind and solar, there is no industrial standard device, or devices, for wave energy conversion and this diversity is transferred to the PTO system. The majority of current WEC PTO systems incorporate a mechanical or hydraulic drive train, power generator, and an electrical control system. The challenge of WEC PTO designs is designing a mechanical-to-electrical component that can efficiently convert irregular, bi-directional, low frequency and low alternating velocity wave motions. While gross average power levels can be predicted in advance, the variable wave elevation input has to be converted into smooth electrical output and hence usually necessitates some type of energy storage system, such as battery storage, accumulator super capacitors, etc., or other means of compensation such as an array of devices. One of the primary challenges for wave energy converter systems is the fluctuating nature of wave resources, which require WEC components to be designed to handle loads (i.e. torques, forces, and powers) that are many times greater than the average load. This approach requires a much greater PTO capacity than the average power output and indicates a higher cost. In addition, supporting mechanical coupling and or gearing can be added to the PCC to help alleviate the difficulties with transmission and control of fluctuating large loads with low frequencies (indicative of wave forcing) into smaller loads at higher frequencies (optimum for conventional electrical machine design) can quickly increase the complexity of the PCC which could result in a greater number of failure modes and increased maintenance costs. All of the previous points demonstrate how the PTO influences WEC dynamics, reliability, performance and cost which are critical design factors. This paper further explores these topics by providing a review of the state-of-the-art PTO systems currently under development, how these novel PTO systems are tested and derisked prior to precommercial deployment, and the evaluation metrics historically used to differentiate between PTO designs and how they can be improved to support control co-design focused development of wave energy systems.

laboratory testing↗

Real-Time Inertia Estimation Tool Implementation Based on Probing Signals

As renewable energy penetration increases and the traditional generators retire in power grids, system inertia decreases and exhibits significant daily fluctuations. Furthermore, fast frequency responses (FFRs) provided by the inverter-based resources (IBRs) begin to playa very critical role and bring new challenges to real-time system inertia monitoring due to the difficulties of quantifying its artificial inertia contribution. Thus, necessitating an accurate real-time inertia estimation tool will not only benefit the secure power grid operations, but also provide insights on assessing the artificial inertia contribution from the IBRs. This paper presents a probing-based real-time inertia estimation tool that has been validated through a power-hardware-in-the-Ioop (PHIL) test system using identical hardware battery energy storage system (BESS) and control in an actual power grid. Preliminary results indicate high estimation accuracy of the developed tool and pave the way for the field test and deployment.

inverter-based resources↗

Hybrid Fuel Cell Systems for Heavy-Duty Trucks: Configuration, Heat Rejection, and Performance

Low-temperature polymer electrolyte membrane fuel cell systems can achieve higher efficiency than diesel engines, but heat rejection remains a major challenge in class-8 heavy-duty fuel cell trucks. For the same rated power, the radiator heat load is greater than that in a diesel engine, while the allowable operating temperatures are lower. This work proposes and evaluates 400 kWe fuel cell–battery hybrid (FCH) platforms and operating strategies that manage heat rejection without enlarging the radiator frontal area. Three FCH platforms are identified, each varying in fuel cell system (FCS) rated power, battery energy storage system (ESS) capacity, and maximum stack coolant exit temperature (T h1 ). All three satisfy key system and vehicle requirements, including 175 kWe FCS power at top sustained speed, 400 kWe FCH power on a 6% grade climb, a target stack power density (PD) of 750 mW e /cm 2 , and heat rejection constraints. The first FCH has the smallest FCS, the largest ESS, and a T h1 of 90 °C. The second achieves the highest PD of 840 mW e /cm 2 at a T h1 of 95 °C. The third has the largest FCS, the smallest ESS, and a T h1 of 102 °C. At a Th1 of 115 °C, the platform can be configured as a stand-alone 400 kWe(net) FCS without hybridization, but the achievable PD drops to 460 mW e /cm 2 .

25 ENERGY STORAGE↗

The Gandalf Staff: A Mobile Tool for Lunar Exploration (2nd year of development)

The Gandalf Staff is a mobile tool designed to be a flexible device supporting crewed and uncrewed operations on the lunar surface. The core of the device is a 24v battery with communications and data storage systems. Initial optional components supporting crewed Extra-Vehicular Activity (EVA) include a LiDAR and 360˚ camera. These provide 3D mapping of the traverse for documentation, and to aid future planning. The mapping also creates outreach opportunities for the public to “stand beside” the astronaut in Virtual Reality (VR). The staff provides external lighting for field site illumination in the south polar region low sun angle environment. Navigation instruments for crew position determination with Lunar Search and Rescue (LunaSAR) are also included. The staff itself can be used as a walking aid or as a splint for Incapacitated Crew Rescue (ICR). As a stand-alone device, the staff operates as a long duration untended science platform collecting environmental data and sending it to a lunar base station. The stand-alone mode requires connection to an auxiliary power source (e.g. solar array) and energy storage system (e.g. battery), so it could become an electrical recharging station. To make rapid progress in the 1st year, and also to demonstrate innovative project management techniques, NASA guided a private industry partner, T STAR, in leading Capstone Engineering student teams at Texas A&M University (TAMU) for proof-of-concept development and testing. These teams developed the power system and demonstrated successful integration of LiDAR, WiFi communications, and external lighting subsystems. Another industry team at Jacobs Technology prototyped a tripod to hold the staff upright. For the 2nd year (FY’22), NASA will again collaborate with partners to prototype enhanced power and lighting concepts. Year 2 will also add new capability for LunaSAR and geophysical science instrumentation using a heat probe. The heat probe is based upon Apollo heritage but modified to measure subsurface volatile ice regimes at the Artemis landing site. Components of the Gandalf Staff can be developed, tested, and deployed independently, or on the integrated staff, rovers, or utility trailers. The project supports crew safety, lunar sample curation, mission science, and public outreach goals of NASA. PLAIN TEST SUMMARY: Gandalf Staff is a 24v battery powered mobile tool for the lunar surface supporting crewed geologic field site exploration, or as a stand-alone science platform. As a tool assisting astronauts conducting field geology, the Gandalf Staff provides external lighting to illuminate shadowed regions on the surface. It also documents the process of collecting rocks and dust samples using a LiDAR (laser range instrument) and cameras. The staff provides a beacon for emergency location of astronauts and augments communications when the astronauts are blocked by large boulders. As a standalone science platform, the Gandalf Staff provides a power system for instruments and sensors to measure the lunar environment over long periods of time. The staff gathers data and sends the results to a lunar base station for analysis.

External Lighting↗

Power Electronics as an Enabler of the Current and Future Grid: Control Instead of Copper

Power electronics are becoming more pervasive in the power system. Solar, wind and battery energy storage systems regularly connect to the grid using power electronic interfaces. Loads are also increasingly power electronic-based. These changes, coupled with the change in the power system's operating characteristics as more and more renewable energy is integrated into the power system, offer a challenge to power electronic and power system engineers alike - how do we design and operate the power system moving forward?

direct connect↗

A 150-kW 99% Efficient All-Silicon-Carbide Triple-Active-Bridge Converter for Solar-Plus-Storage Systems

Solar-plus-storage systems could effectively mitigate the uncertainties of the photovoltaic (PV) generation and improve system reliability by adding an integrated battery energy storage system. As a three-port bidirectional isolated dc–dc converter with soft-switching capability, the triple-active-bridge (TAB) converter inherently matches the requirements of the solar-plus-storage system. However, challenges still remain in the TAB converter design to further improve system efficiency. In this article, the detailed design, implementation, and demonstration for a silicon carbide (SiC) 150-kW TAB converter are presented. Starting from a brief review of the TAB converter, the modulation scheme, power characteristics, and soft-switching region are analyzed. Then, the detailed design of the H-bridge converter building block is given. Here, to improve the system efficiency, a comprehensive characterization of the SiC gate driver with various external gate resistances is performed to address tradeoffs between switching loss and voltage overshoot during transients, as well as the thermal performance of the H-bridge building block. In addition, the design and characterization of the 20-kHz three-port transformer are also given. Comprehensive experimental studies are conducted on a full-power prototype to verify the proposed design. With a measured 99.1% peak efficiency, the proposed TAB converter can fulfill the requirements for solar-plus-storage applications.

14 SOLAR ENERGY↗