Controlling electrochemical growth of metallic zinc electrodes: Toward affordable rechargeable energy storage systems
Zinc anodes are a powerful platform for understanding metal deposition and for low-cost electrical energy storage.
42 ENGINEERING↗
SEARCH · Engineering Papers
Results for “storage controls”
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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A Comparison of Battery Charge Controller Technologies for Wave Energy Converters
Wave energy is a uniquely challenging field for electrical system designers. High peak and low average power potential with a constantly varying energy input is difficult to harness and control through conventional means. To power the blue economy, low-powered wave energy converters (WECs) need batteries for energy storage. Safely and effectively charging batteries from waves requires a charge controller to properly monitor and control voltage and current going to the battery. Currently, off-the-shelf charge controllers exist for other renewable generation such as wind, hydro, and solar. Two topologies were validated: a buck converter and a pulse width modulation (PWM) charge controller. Using an in-lab dry testbed, wave energy power inputs were simulated to properly validate the effectiveness of existing charge controller technologies, identifying the shortcomings and improvements needed to effectively harness wave energy.
battery storage↗
A Comparison of Battery Charge Controller Technologies for Wave Energy Converters: Preprint
Wave energy is a uniquely challenging field for electrical system designers. High peak and low average power potential with a constantly varying energy input is difficult to harness and control through conventional means. To power the blue economy, low-powered wave energy converters (WECs) need batteries for energy storage. Safely and effectively charging batteries from waves requires a charge controller to properly monitor and control voltage and current going to the battery. Currently, off-the-shelf charge controllers exist for other renewable generation such as wind, hydro, and solar. Two topologies were validated: a buck converter and a pulse width modulation (PWM) charge controller. Using an in-lab dry testbed, wave energy power inputs were simulated to properly validate the effectiveness of existing charge controller technologies, identifying the shortcomings and improvements needed to effectively harness wave energy.
battery storage↗
Resilient and Cost-Effective Hybrid Li-Ion Battery Energy Storage System for Sites with Solar Generation and Electric Vehicle (EV) Charging (CRADA Final Report, CRD-19-00799)
The batteries of an energy storage system designed for multiple use-cases are traditionally selected to withstand the highest rate at which a battery is discharged relative to its maximum capacity (C-rate) and cycling requirements of the most intense use-case. However, this is not cost-effective given that the price of the batteries is strongly correlated to C-rate and cyclability. We propose to establish design guidelines for a hybrid energy storage system and test an edge controller that uses high-power and high-energy batteries for high- cyclability use cases such as frequency regulation and electric vehicle (EV) charging and low-cost batteries for low-power and low-cyclability use cases such as summer peak loads. The three main objectives of this proposal are (i) establishing sizing guidelines for such a hybrid storage system, (ii) installing a hybrid storage system in the Energy Systems Integration Facility (ESIF) sized based on those guidelines, and (iii) testing an edge controller specific for the system through hardware tests.
14 SOLAR ENERGY↗
Equitable Design of Behind-the-Meter Energy Storage Programs: A Customer Perspective
Utility deployment of energy storage is done as a utility-scale asset connected directly to the grid (front of meter) or in partnership with a customer on the customer’s premises (behind the meter). By offering incentives for customers to install behind-the-meter storage, utilities can gain operational control of the assets and operate them in a manner that benefits all customers. Some states have also begun providing increased incentive levels to low-income customers to ensure that they can equitably share in the benefits of behind-the-meter energy storage programs. This paper evaluates different approaches to energy storage procurement from the customer’s perspective and evaluates how behind-the-meter programs can be equitably structured while keeping customers financially indifferent between front-of-meter and behind-the-meter energy storage procurements.
Twitchell, Jeremy B.↗
Control co-design under uncertainty for offshore wind farms: Optimizing grid integration, energy storage, and market participation
Offshore wind farms (OWFs) are set to significantly contribute to global decarbonization efforts. Developers often use a sequential approach to optimize design variables and market participation for grid-integrated offshore wind farms. However, this method can lead to sub-optimal system performance, and uncertainties associated with renewable resources are often overlooked in decision-making. Here, this paper proposes a control co-design approach, optimizing design and control decisions for integrating OWFs into the power grid while considering energy market and primary frequency market participation. Additionally, we introduce optimal sizing solutions for energy storage systems deployed onshore to enhance revenue for OWF developers over time. This framework addresses uncertainties related to wind resources and energy prices. We analyze five U.S. west-coast offshore wind farm locations and potential interconnection points, as identified by the Bureau of Ocean Energy Management (BOEM). Results show that optimized control co-design solutions can increase market revenue by 3.2% and provide flexibility in managing wind resource uncertainties.
Control Co-design↗
To Derive or Not to Derive: I/O Libraries Take Charge of Derived Quantities Computation
The ever-increasing volume of data produced by HPC simulations necessitates scalable methods for data exploration and knowledge extraction. Scientific data analysis often involves complex queries across distributed datasets, requiring manipulation of multiple primary variables and generating derived data that needs to be handled efficiently, creating challenges for applications that need to parse many large datasets. Relying on individual applications to handle all intermediate data generally leads to redundant computations across studies and unnecessary data transfers. In this paper, we investigate the performance of different approaches where applications define derived variables as quantities of interest (QoIs) and offload the computation and transfer of these QoIs to the I/O library. This significantly reduces redundancy and optimizes data movement across the distributed storage and processing infrastructure by allowing control over when and where derived variables are computed. We present a detailed analysis of the performance-storage trade-offs associated with different solutions and showcase results for our study on two large-scale datasets created from climate and combustion simulations.
Gainaru, Ana↗
Grid Resiliency with a 100% Renewable Microgrid
San Diego Gas & Electric Company (SDG&E) installed America’s first and largest utility-scale microgrid in Borrego Springs in 2013. The first generation Borrego Springs Microgrid utilized diesel generators to form and stabilize the microgrid island, with support from grid-scale batteries and local solar photovoltaic (PV) generation. In this project, SDG&E in partnership with National Renewable Energy Laboratory (NREL) demonstrated through modeling, simulation and utility field testing that blackstart and islanding of the microgrid can be led with 100% renewable, inverter based resources (IBRs), to help reduce community reliance on conventional generation resources. Through equipment upgrades, grid-forming island leader capability was transitioned to a battery IBR instead of the Borrego Springs Microgrid diesel generators. A new microgrid controller was integrated to the microgrid and programmed to control and manage multiple energy storage systems. Synchrophasor and other power quality data verified autonomous, high-speed response of the IBRs through blackstart, islanding, and load step testing. Results of project field evaluations provide distribution systems operators (DSO) with increased confidence that renewable, IBR can replace traditional generators to blackstart and island microgrids and rapidly establish stable island frequency with rapid changes in peak power demand. Importantly, the project validated the integration feasibility of a distributed energy resource management system (DERMS) controller that manages multiple grid-forming and grid-following IBRs, establishing a standard design interface to reduce the complexity of integrating new DERs in the future and supporting replication by the industry. As a result of learnings in this project, SDG&E has implemented the microgrid controller strategy at multiple other microgrid sites, thereby validating the replicability of the solution. Hardware-in-the-loop (HIL) simulations including power and controller HIL hardware — along with electromagnetic transient (EMT) simulations of Borrego Springs Microgrid —informed adjustments to inverter parameters and were important to characterize the performance of the IBRs in relevant operating conditions before deployment. The EMT and HIL simulations of islanding the entire community are important contributions in providing confidence in IBR performance prior to future islanding of the community in the field. High-fidelity EMT and/or HIL simulation of IBRs can de-risk field operations, and its relevance and importance as a tool is increasing as distribution grids and microgrids become more complex and dynamic with an increasing proportion of renewable generation, distributed energy storage, and two-way power and energy flows.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Systems and methods for providing lockless bimodal queues for selective packet capture
In a network system, an application receiving packets can consume one or more packets in two or more stages, where the second and the later stages can selectively consume some but not all of the packets consumed by the preceding stage. Packets are transferred between two consecutive stages, called producer and consumer, via a fixed-size storage. Both the producer and the consumer can access the storage without locking it and, to facilitate selective consumption of the packets by the consumer, the consumer can transition between awake and sleep modes, where the packets are consumed in the awake mode only. The producer may also switch between awake and sleep modes. Lockless access is made possible by controlling the operation of the storage by the producer and the consumer both according to the mode of the consumer, which is communicated via a shared memory location.
Ros-Giralt, Jordi↗
Selective Ion Enrichment and Charge Storage through Transport Hysteresis in Conical Nanopipettes
Greater selectivity and controls in the ion transport dynamics are essential in fields such as charge storage, separation, energy storage and conversion, neuromorphic computing and learning, electrochemistry, to name a few. Mechanistic insights into the intriguing hysteresis effects in the rectified electrokinetic transport through single conical nanopipettes are unveiled by combining time-resolved electroanalytical experiments with numeric simulation. Cations as counterions for surface charges are found to dominate not just the through-nanopore flux but also the hysteresis charges, that is, the net enriched or expelled charges during the transport process. Built on our earlier report on the through-nanopore ion flux dominated by counterions for surface charges, the “trapped ions” or hysteresis charges are analyzed herein. Cation selectivity is almost 100% in the hysteresis charges during the potential scans in low conductivity states driven by the combined applied and intrinsic surface electrical fields. Surprisingly, the cation selectivity in the total hysteresis charges remains high at 70–80% over a wide bulk concentration range in the high conductivity (HC) states, where higher ionic strength due to ion enrichment would decrease electrostatistic effects and thus ion selectivity. The retained high selectivity at HC is explained by the competition effects of electroosmotic flow against the co-ion migration. Furthermore, the respective cation and anion portions in the total hysteresis charges over a wide range of ionic strength and measurement conditions provide generalizable strategies for improvements in both transport throughput and selectivity.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Two-Stage Deep Reinforcement Learning for Distribution System Voltage Regulation and Peak Load Management
The growing integration of distributed solar photovoltaic (PV) in distribution systems could result in adverse effects during grid operation. This paper develops a two-agent soft actor critic-based deep reinforcement learning (SAC-DRL) solution to simultaneously control PV inverters and battery energy storage systems for voltage regulation and peak demand reduction. The novel two-stage framework, featured with two different control agents, is applied for daytime and nighttime operations to enhance control performance. Comparison results with other control methods on a real feeder in Western Colorado demonstrate that the proposed method can provide advanced voltage regulation with modest active power curtailment and reduce peak load demand from feeder's head.
deep reinforcement learning↗
Energy-Storage Fed Smart Inverters for Mitigation of Voltage Fluctuations in Islanded Microgrids
The continuous integration of intermittent low-carbon energy resources makes islanded microgrids vulnerable to voltage fluctuations. Besides, different dynamic response of synchronous-based and inverter-based distributed generation (DG) units can result in an instantaneous power imbalance between supply and demand during transients. As a result, the ac-bus voltage of microgrid starts oscillating which might have severe consequences such as blackouts. This paper modifies the conventional control scheme of battery energy storage systems (BESSs) to participate in improving the dynamic behavior of islanded microgrids by mitigating the voltage fluctuations. A piecewise linear-elliptic (PLE) droop is proposed and employed in BESS to achieve an enhanced voltage profile by injecting/absorbing reactive power during transients. In this way, the conventional inverter implemented in BESS turns into a smart inverter to cope with fast transients. Using the proposed approach in this paper, any linear droop curve with a specified coefficient can be replaced by a PLE droop curve. Compared with linear droop, an enhanced dynamic response is achieved by utilizing the proposed PLE droop. Case study results are presented using PSCAD/EMTDC to demonstrate the superiority of the proposed approach in improving the dynamic behavior of islanded microgrids.
Pilehvar, Mohsen S.↗
Techno-Economic Analysis for the Addition of a Thermal Energy Storage System to a Central Plant
Increasing energy demand and rising peak loads present significant challenges for energy management in commercial and institutional settings. As climate change drives greater cooling needs, central plants must navigate the complex tradeoffs between operational efficiency, cost control, and grid stability. Thermal energy storage (TES) systems offer a viable solution by shifting energy consumption from peak to off-peak periods, thereby reducing peak demand, lowering utility expenses, and improving grid resilience. However, the success of TES implementation hinges on appropriate system sizing, effective control strategies, and alignment with local utility rate structures. This article presents a techno-economic analysis of integrating a chilled water TES system into the central plant at California State University, Dominguez Hills. Drawing on historical load profiles and utility tariffs, we assess three TES sizing approaches and their corresponding control strategies from both energy and economic perspectives. This article utilizes a model-based approach to assess the impact of TES sizing and control strategies on the techno-economic feasibility of integrating TES into an existing central plant. The models employed for this analysis were calibrated using 4 years of historical data. Here, the results demonstrated that utility tariffs and the campus's operational profiles dictate the most feasible sizing and control methods. The findings offer valuable insights for institutions and commercial building managers exploring sustainable energy solutions. By demonstrating how optimized TES strategies can improve operational efficiency while achieving financial savings, this study highlights the potential for TES to align performance with cost effectiveness in real-world applications.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
A Novel Grid-forming Voltage Control Strategy for Supplying Unbalanced Microgrid Loads Using Inverter-based Resources
This paper presents a novel grid-forming voltage control strategy for a battery energy storage system to maintain balanced three-phase output voltages when serving unbalanced loads. A stationary reference frame (αβ) based control scheme is proposed to regulate positive-sequence and negative-sequence voltages. Compared with the conventional rotating reference frame (dq) based control scheme, the proposed scheme shows better dynamic performance. Then, we analyze the system zero-sequence network and propose adding a grounding transformer to the Y/Yg connected output transformer to further reduce the voltage unbalance at the point of common coupling. The simulation results demonstrate the effective performance of the proposed voltage control scheme. Based on the results, the power-voltage unbalance curve is derived for different output transformer configurations to establish the relationship between the power unbalance limit and the voltage unbalance limit for microgrid power scheduling.
Xu, Bei↗
Two-Stage Deep Reinforcement Learning for Distribution System Voltage Regulation and Peak Load Management: Preprint
The growing integration of distributed solar photovoltaic (PV) in distribution systems could result in adverse effects during grid operation. This paper develops a soft actor critic-based deep reinforcement learning (SAC-DRL) solution to simultaneously control PV inverters and battery energy storage systems for voltage regulation and peak load demand shaving. The novel two-stage framework, featured with two different control agents, is applied for daytime and nighttime operation to enhance the control performance. Comparison results with other control methods on a real feeder in Western Colorado demonstrate that the proposed method can provide advanced voltage regulation with modest active power curtailment for peak demand reduction.
deep reinforcement learning↗
Vulnerability Detection Methodology for a Digital Signal Processor Micro-Controller at Edge Level Distributed Energy Resource Controller
Integration of renewable energy and energy storage based Distributed Energy Resource (DER) assets to the grid has seen an upsurge over the past few decades. Traditionally, energy would be generated at a thermal or nuclear power plant and then transmitted over long distance through the transmission grid and then supply to the customers at the distribution grid. Modern renewable energy based DER assets have brought energy production at the edge of distribution grid, which has made the grid vulnerable to cyber intrusion as the power flow controllers for such DER assets are now distributed across the grid from distribution level to transmission level. This paper discusses the vulnerability posed by such power flow controllers of DER assets and demonstrates a detection methodology for unauthorized access and manipulation of system configuration for Digital Signal Processor based Microcontrollers. Experimental results proving efficacy of the methodology have been shown in paper.
Bhowmik, Pankaj↗
Microgrid Power Flow Control with Integrated Battery Management Functions
This paper presents a microgrid power flow control for islanded microgrids consisting of photovoltaic (PV) system, battery energy storage system (BESS) and diesel generator. The proposed control ensures automatic coordination among the microgrid units. To achieve the coordination, the local control of PV system and BESS are configured to regulate power flow without relying on communications. The generator is controlled as the back-up source and its connection depends on battery state of charge (SOC). A state machine with hysteresis settings is used to trigger smooth transition among operation modes. Not only does the proposed controller consider battery SOC and power limits, but also integrates three-stage charging control as battery charging mechanism, which is not considered in the existing literature. The proposed control is validated for an islanded hybrid microgrid using an OPAL-RT real-time simulator.
Long, Qian↗
HyMARC Seedling: Optimized Hydrogen Adsorbents via Machine Learning and Crystal Engineering
This final report is presented in two parts: In Part 1, the H 2 capacities of a diverse set of 918,734 metal-organic frameworks (MOFs) sourced from 19 databases is predicted via machine learning (ML). Using only 7 structural features as input, ML identifies 8,282 MOFs with the potential to exceed the capacities of state-of-the-art materials. The identified MOFs are predominantly hypothetical compounds having low densities (<0.31 g/cm3) in combination with high surface areas (>5,300 m2/g), void fractions ($0.90), and pore volumes (>3.3 cm3/g). The relative importance of the input features are characterized, and dependencies on the ML algorithm and training set size are quantified. The most important features for predicting H 2 uptake are pore volume (for gravimetric capacity) and void fraction (for volumetric capacity). The ML models are available on the web, allowing for rapid and accurate predictions of the hydrogen capacities of MOFs from limited structural data; the simplest models require only a single crystallographic feature. In part 2, ways to improve the poor powder packing density of MOFs is discussed. More specifically, a strategy that improves packing efficiency and volumetric hydrogen gas storage density dramatically through engineered morphologies and controlled-crystal size distributions is presented that holds promise for maximizing storage capacity for a given MOF. The packing density improvement, demonstrated for the benchmark sorbent MOF-5, leads to a significant enhancement of volumetric hydrogen storage performance relative to commercial MOF-5. System model projections demonstrate that engineering of crystal morphology/size or use of a bimodal distribution of cubic crystal sizes in tandem with system optimization can surpass the 25 g/L volumetric capacity of a typical 700 bar compressed storage system and exceed the DOE targets 2020 volumetric capacity (30 g/L). Finally, a critical link between improved powder packing density and reduced damage upon compaction is revealed leading to sorbents with both high surface area and high density.
08 HYDROGEN↗