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Faster-than-real-time Simulation with Demonstration for Resilient DER Integration
The US electric grid is facing operational, stability, and security challenges. Transmission system operators need some measure of visibility into distribution system renewable generation. Distribution system generation needs to support transmission system voltage. The grid is experiencing an expansion in measurement systems. How to take full advantage of this expansion and defend against attacks, both cyber and physical, poses additional challenges. The Faster-than-real-time Simulation with demonstration for Resilient DER Integration project set out to do the following: a. Flatten the voltage profile through the feeders and system for cost saving and voltage stabilization needs. b. Increase the amount of intermittent distributed energy resources (IDERs) that could be deployed on a utility feeder and provide 100% or more energy needed for the demands on that feeder, and c. based on an accurate model (Digital Twin) of the utilities system, be able to detect any abnormalities on the utilities distribution system. To manage the voltage and increase IDER penetration (a,b), Graph Trace Analysis is employed in a time-series, optimal power flow to coordinate the time-varying feedback control setpoints of a distribution feeder’s utility control devices. Under the coordinated control are a Load Tap Changing Transformer, a voltage regulator, and five switched capacitor banks. The feeder serves over 2000 customers, the feeder secondaries are modeled, and the feeder has 2.3 MW of PV generation, corresponding to a 17.4% penetration of PV generation. The feeder model has over 12,000 components, where every customer load bus and PV generator are modeled. The accuracy of the power flow solution is compared against historical meter voltage measurements, the improvement in conservation voltage reduction energy savings as a function of the coordinated control desired voltage profile is investigated, and the increase in PV penetration of the coordinated control over the existing control is presented. To achieve improved control performance while observing system operation constraints, bellwether Advanced Metering Infrastructure (AMI) voltage measurements are used to adjust the desired voltage profile used by the optimal power flow analysis. To detect and alleviate or negate attacks or failures on the distribution and transmission utility grids (c) the grid needs to be resilient and self-healing. In this project software was designed to do just that. At the center of the software is an Integrated System Model (ISM) that spans from transmission to secondary distribution. The ISM is employed in real-time abnormality detection, voltage stability forecasting, and multi-mode control. Testing results are presented for: 1—attacks on utility infrastructure; 2—energy savings from optimal control; 3—distribution system control response during a low voltage transmission system event; 4—cyber-attacks on PV inverters, where physical inverters are used in hard-ware-in-the-simulation-loop studies. Contributions of this work include real-time analysis that spans from three-phase transmission through secondary distribution; an approach for detecting abnormalities that employs measurements from three independent measurement systems; and a multi-mode distribution system control that responds to cyber-attacks, physical attacks, equipment failures, and transmission system needs.
A Combined Day-ahead and Real-time approach for Scheduling Real and Reactive power dispatch of Battery Energy Storage System
A combined day-ahead (look-ahead) and real-time optimization approach is proposed to schedule BESS real and reactive power dispatch. The look ahead scheduling agent maximizes profit and essentially performs the function of energy arbitrage by looking at forecasted prices, network constraints and BESS operating conditions to generate real power dispatch schedule. While this schedule is followed, the real time optimization agent computes an optimum reactive power set-point (for a particular objective function) using optimal power flow framework. The concepts are implemented by co-simulating legacy tools and demonstrated on a large real world distribution system. In addition to the profits from energy arbitrage, optimum reactive power set point computation results in achieving desirable network attributes like flat voltage profiles and minimizing voltage deviations from desired value.
Volt-VAR Optimization in Distribution Networks Using Twin Delayed Deep Reinforcement Learning
Modern distribution grids are undergoing new challenges due to the stochastic nature of distributed energy resources (DERs). High penetration of DERs has a significant impact on Volt-VAR profile and system power losses. This work proposes a deep reinforcement learning (DRL)-based Volt-VAR optimization approach for improving voltage profile and reducing system power loss under high penetration of distributed energy resources, such as battery energy storage and solar photovoltaic units in distribution grids. The twin delayed deep deterministic policy gradient (TD3) method-based DRL agent is proposed to configure optimal set-points of reactive power outputs of fast responding smart inverters. The agent schedules the reactive power of inverters according to their physical capabilities, such as minimum allowed power factor, e.g., 0.9 leading/lagging. The reward function of the proposed DRL scheme is designed carefully to ensure a proper voltage profile of the grids with effective scheduling of reactive power outputs from inverters. The performance of the proposed model is verified on modified IEEE 34- and 123-bus systems and compared with base case with no reactive supply by inverters, and local droop Volt-VAR control approach. The results show that the proposed method performs better than the local droop control and deep deterministic policy gradient (DDPG)-based DRL method for reducing voltage fluctuation and minimizing power loss.
Coulombic Efficiency and Capacity Retention are Not Universal Descriptors of Cell Aging
Capacity and coulombic efficiency are often used to assess the performance of Li-ion batteries, under the assumption that these quantities can provide direct insights about the rate of electron consumption due to growth of the solid electrolyte interphase (SEI). Here, we show that electrode properties can actually change the amount of information about aging that can be directly retrieved from capacity measurements. During cycling of full-cells, only portions of the voltage profiles of the positive and negative electrodes are accessible, leaving a reservoir of cyclable Li + stored at both electrodes. The size and availability of this reservoir depends on the shape of the voltage profiles and accessing this extra Li + can offset some of the capacity that is consumed by the SEI. Consequently, capacity and efficiency measurements can, at times, severely underestimate the rate of side reactions experienced by the cell. We show, for example, that a same rate of SEI growth would cause faster capacity fade in LiFePO 4 than in NMC cells, and that the perceived effects of aging depend on testing variables such as depth of discharge. Simply measuring capacity may be insufficient to gauge the true extent of aging endured by Li-ion batteries.
Demonstrating Distribution System Resiliency through Grid-Edge Microgrids, on a Multi-Site Networked Hardware-in-Loop Platform
With the increasing penetration of Distributed Energy Resources (DERs) at the grid-edge, power systems include more energy storage, remote switches, relays, voltage regulators, and other intelligent electronic devices (IED). Effective control of these grid-edge devices by using Advanced Distribution Management Systems (ADMS) can yield substantial improvements to the resiliency and power quality of distribution systems. In this paper, improvements to the resiliency of a distribution system are demonstrated using a multi-site evaluation environment consisting of a real-time Hardware-in-Loop (HIL) setup in which DERs and other IEDs are modeled; and an ADMS which monitors and is able to control the distribution system assets. The HIL model and the ADMS are located 2400 km away, with communication between the sites enabled by a data manager using Distributed Network Protocol 3 (DNP3), demonstrating the system's capabilities even over long distances. After a simulated transmission system failure in the HIL demonstration setup, DERs and other devices are operated to restore critical loads and node voltage profile (to within the 'nominal +/-5%' band) in the distribution system.
Formation of LiF Surface Layer During Direct Fluorination of High-Capacity Co-Free Disordered Rocksalt Cathodes
Disordered rocksalt (DRX) cathodes have attracted interest due to their high capacity and compositional flexibility (e.g., Co-free chemistries). However, the sloping voltage profile and gradual capacity fade during cycling have hindered widespread adoption of these materials. Simulations predict that fluorine substitution in DRX cathodes will improve their capacity, rate performance and cyclability. In this study we use a fluidized bed reactor to fluorinate a model Lirich DRX composition (Li1.15Ni0.375Ti0.375Mo0.1O2, NTMO) to investigate how fluorine content impacts the cathode’s structure and electrochemical performance. Instead of substituting O with F to form oxyfluoride phases, direct fluorination of DRX cathodes leads to formation of LiF surface films which improves the specific energy and capacity retention. This study demonstrates the feasibility of direct fluorination to improve the electrochemical performance of high voltage cathodes by tuning the material’s surface chemistry.
Voltage-Based Strategies for Preventing Battery Degradation under Diverse Fast-Charging Conditions
Maintaining safe operating conditions is a key challenge for high-performance lithium-ion battery applications. The lithium-plating reaction remains a risk during charging, but limited studies consider the highly variable charging conditions possible in commercial cells. Here we combine pseudo-2D electrochemical modeling with data visualization methods to reveal important relationships between the measurable cell voltage and difficult-to-predict Li-plating onset criteria. An extensively validated model is used to compute Li plating for thousands of multistep charging conditions spanning diverse rates, temperatures, states-of-charge, and cell aging. Here we observe an empirical cell operating voltage limit below which plating does not occur across all conditions, and this limit varies with the battery state-of-charge and aging. A model sensitivity analysis also indicates that, when comparing two charging voltage profiles, the capacity difference at 4.0 V correlates well with the difference in the plating onset capacity. These results encourage simple strategies for Li-plating prevention that are complementary to existing battery controls.
Stable-Cycling Sustainable Na-Ion Batteries with Olivine Iron Phosphate Cathode in an Ether Electrolyte
Sustainable batteries using nontoxic, earth-abundant, and low-cost materials are key to decarbonization. Olivine NaFePO 4 fulfills these criteria, is attractive for Na-ion batteries, and can be derived from LiFePO 4 recycled from Li-ion battery wastes. Critical knowledge is needed for transforming LiFePO 4 to NaFePO 4 to enable such a sustainable, green engineering path toward high-performance Na-ion batteries. Herein, we report on the development of a stable-cycling, sustainable olivine iron phosphate-based Na-ion battery empowered by an improved understanding of materials transformation and electrolyte chemistry. First, we found that the conventional carbonate electrolyte with fluoroethylene carbonate additive causes an additional plateau (~2.4 V) at the end of the discharge process of the FePO 4 ||Na metal cell, leading to lower initial discharge capacity and voltage. This result shows that the voltage profile is influenced by not only intrinsic materials phase transformation during battery cycling but also the electrolyte additives and interphases formed. With the 1 M NaPF 6 diglyme electrolyte, we achieved an excellent capacity retention of 96% and 98% after 500 cycles at 1 and 5 C, respectively. Second, we chemically sodiated FePO 4 to form single-phase Na 0.9 FePO 4 . Na 0.9 FePO 4 ||hard carbon full cells demonstrated a remarkable capacity retention of ~84% at 3 and 5 C after 1000 cycles. The successful implementation of hard carbon, which can be derived from biomass waste, will further improve the sustainability of energy storage technologies. Our research demonstrates that electrolyte chemistry influences the voltage profile of phase-changing electrodes and provides effective electrolyte and full-cell design solutions for stable-cycling NaFePO 4 .
Modeling Intercalation Chemistry with Multiredox Reactions by Sparse Lattice Models in Disordered Rocksalt Cathodes
Modern battery materials can contain many elements with substantial site disorder, and their configurational state has been shown to be critical for their performance. The intercalation voltage profile is a critical parameter to evaluate the performance of energy storage. The application of commonly used cluster expansion techniques to model the intercalation thermodynamics of such systems ab initio is challenged by the combinatorial increase in configurational degrees of freedom as the number of species grows. Such challenges necessitate the efficient generation of lattice models without overfitting and proper sampling of the configurational space under the requirement of charge balance in ionic systems. In this work, we introduce a combined approach that addresses these challenges by (1) constructing a robust cluster expansion Hamiltonian using the sparse regression technique, including -norm regularization and structural hierarchy; and (2) implementing semigrand-canonical Monte Carlo to sample charge-balanced ionic configurations using the table-exchange method and an ensemble average approach. These techniques are applied to a disordered rocksalt oxyfluoride (LMNOF) that is part of a family of promising earth-abundant cathode materials. The simulated voltage profile is found to be in good agreement with experimental data and particularly provides a clear demonstration of the and oxygen contributions to the redox potential as a function of content.
Deep reinforcement learning assisted co-optimization of Volt-VAR grid service in distribution networks
With the increasing penetration of distributed energy resources in distribution networks, Volt-VAR control and optimization (VVC/VVO) have become very important to ensure an acceptable quality of service to all customers. System operators can rely on slow-responding utility devices, including capacitor banks and on-load tap changing transformers, along with fast-responding battery and photovoltaic (PV) inverters for the VVC/VVO implementation. Because of variations in response time of these two classes of devices, and different control actions (discrete versus continuous), coordinated and optimal scheduling and operation have become of utmost importance. Here, this paper develops a look-ahead deep reinforcement learning (DRL)-based multi-objective VVO technique to improve the voltage profile of active distribution networks, decrease network and inverter power loss, and save the operational cost of the grid. It proposes a deep deterministic policy gradient (DDPG)-based approach to schedule the optimal reactive and/or active power set-points of fast-responding inverters, and a deep Q-network (DQN)-based DRL agent to schedule the discrete decisions variables of slow-responding assets. The reactive power output of PV and battery smart inverters are scheduled at 30-minute intervals and the capacitors’ commitment status is scheduled with several hour intervals. The proposed framework is validated on the modified IEEE 34-bus and 123-bus test cases with embedded PV and PV-plus-storage. To validate the efficacy of the proposed VVO, it is compared with several scenarios, including the base case without VVO, localized droop control of DERs, DDPG-only, and twin delayed DDPG (TD3) agent-based DRL techniques. The results justify the superior performance of the proposed method to improve the voltage profile, reduce network power loss, and minimize the look-ahead grid operational cost while minimizing the undesirable power losses in inverters as a result of power factor adjustments.
Ligand Substituent Effects on the Electronic Properties of Lindqvist‐Type Polyoxometalate Multi‐Level‐Switches in the Gas Phase, Solution and on Surfaces
Abstract Although the intrinsic electronic properties of polyoxometalates (POMs) can be greatly influenced by modifying them with organic substituents, their resistive switching behavior on surfaces dependent on the organic substituents remains largely unexplored. In this work, we assessed the importance of electron‐withdrawing and electron‐donating ligand substituents on the material properties of a series of hybrid Lindqvist‐type hexavanadates TBA 2 [V 6 O 13 ((OCH 2 ) 3 CCH 2 OH) 2 ] (TBA 2 V 6 ‐OH), TBA 2 [V 6 O 13 ((OCH 2 ) 3 CMe) 2 ] (TBA 2 V 6 ‐Me), TBA 2 [V 6 O 13 ((OCH 2 ) 3 CNHCOCH 2 Cl) 2 ] (TBA 2 V 6 ‐Cl), and TBA 2 [V 6 O 13 ((OCH 2 ) 3 CNHCOCH 2 ‐OOCC 10 H 15 ) 2 ] (TBA 2 V 6 ‐Ad) as potential resistive random‐access memory (ReRAM) components. Compared to their redox behavior in solution, changing the ligand substituents on surfaces results in no significant effect on the potential and, thus, no effect on the resistance steps in the current‐voltage profiles. However, while the current‐voltage characteristics do not change, the peripheral metal‐free substituents in the trisalkoxide framework of Lindqvist‐type hexavanadate molecules influence the adsorption and switching stability of these POMs on gold. This work highlights the noticeable differences between hexavanadate's redox properties in solution (which follow the trend observed in the gas phase) and hexavanadate's resistive switching properties on conducting surfaces. Importantly, their multi‐state switching behavior is not significantly altered by the different type of substituent at the periphery of the trisalkoxo ligands.
Comparison of Probabilistic Forecasts for Predictive Voltage Control
This paper explores predictive cooperative voltage control in distribution systems with highly variable sources such as photovoltaics (PV). The goal is to maintain the voltage profile within the limits despite the fluctuations due to sudden changes in solar power generation. The predictive voltage control method relies on probabilistic solar power and load forecasts to select the optimum Voltage Regulator (VR) taps appropriately. VR taps are selected to minimize the risk of voltage violation. A modified version of the IEEE 123 system is used as the case study. A 100% penetration of solar power is assumed for the distribution system with profiles for solar generation and loads added to the system. Three different probabilistic forecast models (Quantile Regression (QR), Gaussian distribution and volatility forecasting using Generalized Autoregressive Conditional Heteroskedasticity (GARCH)) are explored in this study. The results for the VR taps and Voltage Deviation Index (VDI) are compared to find the most effective forecast model.
A hybrid architecture for volt-var control in active distribution grids
Modern active distribution grids are characterized by the increasing penetration of distributed energy resources (DERs). The proper coordination and scheduling of a large numbers of these small-scale and spatially distributed DERs is necessary, and warrants the use of novel distributed approaches. In this paper, we propose a hybrid volt-var control architecture for the distribution grid, which leverages existing centralized and local approaches to planning, decision making, and control, and augments it with distributed optimization and distributed control for DER management. First, we propose a convex model to describe the power physics of distribution grids of meshed topology and unbalanced structure, based on current injection and McCormick Envelopes. Second, we employ the distributed proximal atomic coordination (PAC) algorithm to coordinate DERs to provide voltage support. We implement volt-var optimization by optimally coordinating DERs including PV smart inverters and demand response. We present results using the IEEE-34 bus network, using real data from a distribution feeder in Hawaii, to model load and PV generation. Different levels of DER penetration and objective functions are simulated. Finally, our results show the need for the coordination of DERs to improve voltage profiles, even in networks with existing voltage control devices. Further, we show the need for flexible reactive power capabilities to achieve desired grid performance.
Ultrahigh-Capacity Rocksalt Cathodes Enabled by Cycling-Activated Structural Changes
Mn-redox-based oxides and oxyfluorides are considered the most promising earth-abundant high-energy cathode materials for next-generation lithium-ion batteries. While high capacities are obtained in high-Mn content cathodes such as Li- and Mn-rich layered and spinel-type materials, local structure changes and structural distortions ( often lead to voltage fade, capacity decay, and impedance rise, resulting in unacceptable electrochemical performance upon cycling. In the present study, structural transformations that exploit the high capacity of Mn-rich oxyfluorides while enabling stable cycling, in stark contrast to commonly observed structural changes that result in rapid performance degradation, are reported. Here, it is shown that upon cycling of a cation-disordered rocksalt (DRX) cathode (Li 1.1 Mn 0.8 Ti 0.1 O 1.9 F 0.1 , an ultrahigh capacity of ≈320 mAh g –1 (energy density of ≈900 Wh kg –1 ) can be obtained through dynamic structural rearrangements upon cycling , along with a unique voltage profile evolution and capacity rise. At high voltage, the presence of Mn 4+ and Li + vacancies promotes local cation ordering, leading to the formation of domains of a “δ phase” within the disordered framework. On deep discharge, Mn 4+ reduction, along with Li + insertion transform the structure to a partially ordered DRX phase with a β'-LiFeO 2 -type arrangement. At the nanoscale, domains of the in situ formed phases are randomly oriented, allowing highly reversible structural changes and stable electrochemical cycling. These new insights not only help explain the superior electrochemical performance of high-Mn DRXbut also provide guidance for the future development of Mn-based, high-energy density oxide, and oxyfluoride cathode materials.
Deep learning of experimental electrochemistry for battery cathodes across diverse compositions
Artificial intelligence (AI) has emerged as a tool for discovering and optimizing novel battery materials. However, the adoption of AI in battery cathode representation and discovery is still limited due to the complexity of optimizing multiple performance properties and the scarcity of high-fidelity data. Here, we present a machine learning model (DRXNet) for battery informatics and demonstrate the application in the discovery and optimization of disordered rocksalt (DRX) cathode materials. We have compiled the electrochemistry data of DRX cathodes over the past 5 years, resulting in a dataset of more than 19,000 discharge voltage profiles on diverse chemistries spanning 14 different metal species. Learning from this extensive dataset, our DRXNet model can capture critical features in the cycling curves of DRX cathodes under various conditions. Our approach offers a data-driven solution to facilitate the rapid identification of novel cathode materials, accelerating the development of next-generation batteries for carbon neutralization.
Computational and experimental search for potential polyanionic K-ion cathode materials
Discovering high-energy cathode materials is critical to construct K-ion batteries for practical applications. Owing to the great success of layered oxides in Li- and Na-ion systems, K layered cathodes have also been investigated in recent years. However, the much larger size of K + compared to Li or Na introduces strong K + –K + interaction within the layer, which results in a sloped voltage profile thereby limiting the specific capacity and operating voltage. In contrast, polyanionic materials with a three-dimensional K + arrangement can effectively mitigate K + –K + interaction. In this work, ten K polyanionic compounds with theoretical capacity >100 mA h g –1 are screened from the Inorganic Crystal Structure Database as potential cathode materials for K-ion batteries. Among the ten proposed compounds, K 2 MnP 2 O 7 , K 2 Mn 2 P 2 O 7 F 2 , K 2 Fe 2 P 2 O 7 F 2 , and K 6 V 2 (PO 4 ) 4 with average voltage <4.5 V are synthesized and evaluated electrochemically. While the re-insertion of K into these compounds is not fully reversible, it may be related to the very high migration barrier that we compute for K ions. In addition, we show the successful synthesis of a series of K 3 V 3–x Cr x (PO 4 ) 4 (x = 0, 1, 2, 3) compounds. Among these, K 3 V 2 Cr(PO 4 ) 4 exhibits the largest reversible capacity, as revealed by the in situ investigation. Lastly, we find that the redox couples in many of these compounds sit at remarkably high potential, even higher than in equivalent Li compounds, which brings both opportunities and challenges in the future research of K polyanion cathodes.
Modeling Framework for Bulk Electric Grid Impacts from HEMP E1 and E3 Effects (Tasks 3.1 Final Report)
This report presents a framework to evaluate the impact of a high-altitude electromagnetic pulse (HEMP) event on a bulk electric power grid. This report limits itself to modeling the impact of EMP E1 and E3 components. The co-simulation of E1 and E3 is presented in detail, and the focus of the paper is on the framework rather than actual results. This approach is highly conservative as E1 and E3 are not maximized with the same event characteristics and may only slightly overlap. The actual results shown in this report are based on a synthetic grid with synthetic data and a limited exemplary EMP model. The framework presented can be leveraged and used to analyze the impact of other threat scenarios, both manmade and natural disasters. This report d escribes a Monte-Carlo based methodology to probabilistically quantify the transient response of the power grid to a HEMP event. The approach uses multiple fundamental steps to characterize the system response to HEMP events, focused on the E1 and E3 components of the event. 1) Obtain component failure data related to HEMP events testing of components and creating component failure models. Use the component failure model to create component failure conditional probability density function (PDF) that is a function of the HEMP induced terminal voltage. 2) Model HEMP scenarios and calculate the E1 coupled voltage profiles seen by all system components. Model the same HEMP scenarios and calculate the transformer reactive power consumption profiles due to E3. 3) Sample each component failure PDF to determine which grid components will fail, due to the E1 voltage spike, for each scenario. 4) Perform dynamic simulations that incorporate the predicted component failures from E1 and reactive power consumption at each transformer affected by E3. These simulations allow for secondary transients to affect the relays/protection remaining in service which can lead to cascading outages. 5) Identify the locations and amount of load lost for each scenario through grid dynamic simulation. This can be an indication of the immediate grid impacts from a HEMP event. In addition, perform more detailed analysis to determine critical nodes and system trends. 6) To help realize the longer-term impacts, a security constrained alternating current optimal power flow (ACOPF) is run to maximize critical load served. This report describes a modeling framework to assess the systemic grid impacts due to a HEMP event. This stochastic simulation framework generates a large amount of data for each Monte Carlo replication, including HEMP location and characteristics, relay and component failures, E3 GIC profiles, cascading dynamics including voltage and frequency over time, and final system state. This data can then be analyzed to identify trends, e.g., unique system behavior modes or critical components whose failure is more likely to cause serious systemic effects. The proposed analysis process is demonstrated on a representative system. In order to draw realistic conclusions of the impact of a HEMP event on the grid, a significant amount of work remains with respect to modeling the impact on various grid components.