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At least 487 records · Page 27

Influence of the air-cooled supercritical carbon dioxide recompression cycle design-point on molten salt power tower techno-economic performance

Here, this paper investigates the techno-economic performance of a molten salt power tower integrated with an air-cooled supercritical carbon dioxide recompression cycle. Prior research into the cycle design point showed trade-offs between efficiency, cost, and temperature difference across the heat input, resulting in trade-offs between the design-point generation of the molten salt power tower system and the cost of sensible-heat thermal energy storage. Prior research into cycle off-design showed that colder ambient temperature designs have higher efficiencies on cold days but lower efficiency and capacity degradation on hot days. We present a framework to evaluate how cycle design, cost, and corresponding off-design performance affect the techno-economic performance of the molten salt power tower system. We use a steady-state off-design cycle model assuming fixed shaft speeds, inventory control, and air-cooler fan power control and integrate the cycle model with the molten salt power tower model from the National Renewable Energy Laboratory's System Advisor Model. First, we present a surrogate model that uses a heuristic approach to correct the interpolation when the cycle reaches capacity limitations at ambient temperatures hotter than design. Next, we evaluate how the recompression cycle design-point ambient temperature, air-cooler approach temperature, air-cooler fan power, and recuperator conductance influence cycle and system design metrics. Finally, we complete annual simulations using an hourly "duck curve" pricing schedule and present annual performance metrics to evaluate the cumulative effects of the cycle design parameters and corresponding off-design performance on molten salt power tower techno-economics.

14 SOLAR ENERGY↗

A Hierarchical OPF Algorithm with Improved Gradient Evaluation in Three-Phase Networks

Linear approximation commonly used in solving alternating-current optimal power flow (AC-OPF) simplifies the system models but incurs accumulated voltage errors in large power networks. Such errors will make the primal-dual type gradient algorithms converge to solutions with voltage violation. In this paper, we improve a recent hierarchical OPF algorithm that rested on primal-dual gradients evaluated with a linearized distribution power flow model. Specifically, we propose a more accurate gradient evaluation method based on an unbalanced three-phase nonlinear distribution power flow model to mitigate the errors arising from linearization. The resultant gradients feature a blocked structure that enables our development of an improved hierarchical primal-dual algorithm to solve the OPF problem. Numerical results on the IEEE 123-bus test feeder and a 4,518-node test feeder show that the proposed method can enhance voltage safety at comparable computational efficiency with the linearized algorithm.

approximation algorithms↗

Dynamics Informed Optimization for Resilient Energy Systems

Optimal mitigation planning for highly disruptive contingencies to a transmission-level power system requires optimization with dynamic power system constraints, due to the key role of dynamics in system stability to major perturbations. We formulate a generalized disjunctive program to determine optimal grid component hardening choices for protecting against major failures, with differential algebraic constraints representing system dynamics (specifically, differential equations representing generator and load behavior and algebraic equations representing instantaneous power balance over the transmission system). We optionally allow stochastic optimal pre-positioning across all considered failure scenarios, and optimal emergency control within each scenario. This novel formulation allows, for the first time, analyzing the resilience interdependencies of mitigation planning, preventive control, and emergency control. Using all three strategies in concert is particularly effective at maintaining robust power system operation under severe contingencies, as we demonstrate on the Western System Coordinating Council (WSCC) 9-bus test system using synthetic multi-device outage scenarios. Towards integrating our modeling framework with real threats and more realistic power systems, we explore applying hybrid dynamics to power systems. Our work is applied to basic RL circuits with the ultimate goal of using the methodology to model protective tripping schemes in the grid. Finally, we survey mitigation techniques for HEMP threats and describe a GIS application developed to create threat scenarios in a grid with geographic detail.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Integrated transient modeling of gas turbine and sCO2 power cycle for exhaust heat recovery application

For distributed generation installations, the transient response of the power generation system is critical to meet power quality (voltage and frequency) requirements. Transient models for gas turbine and supercritical carbon dioxide (sCO2) power cycle were integrated for an exhaust heat recovery bottoming cycle system for a distributed generation application. The transient model for the SGT-750 Siemens gas turbine was a ‘black-box’ functional mockup interface (FMI) model developed by Siemens Industrial Turbomachinery in $Finsp\mathring{a}ng$, Sweden. The SGT-750 is a twin-shaft gas turbine that produces 40 MW electricity with an efficiency of about 40% at ISO conditions. At 100% gas turbine throttle (load), the SGT-750 has average exhaust conditions of 114.6 kg/s and 469.8 °C.

20 FOSSIL-FUELED POWER PLANTS↗

A Machine Learning-Based Vulnerability Analysis for Cascading Failures of Integrated Power-Gas Systems

This article proposes a cascading failure simulation (CFS) method and a hybrid machine learning method for vulnerability analysis of integrated power-gas systems (IPGSs). The CFS method is designed to study the propagating process of cascading failures between the two systems, generating data for machine learning with initial states randomly sampled. The proposed method considers generator and gas well ramping, transmission line and gas pipeline tripping, island issue handling and load shedding strategies. Then, a hybrid machine learning model with a combined random forest (RF) classification and regression algorithms is proposed to investigate the impact of random initial states on the vulnerability metrics of IPGSs. Extensive case studies are carried out on three test IPGSs to verify the proposed models and algorithms. Simulation results show that the proposed models and algorithms can achieve high accuracy for the vulnerability analysis of IPGSs.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Co-Simulation of PSS/E, OpenDSS, and PSCAD for Power Systems Stability Analysis With Inverter-Based Resources: Preprint

The increasing penetration of inverter-based resources (IBRs) is reshaping the dynamic behavior of power systems. IEEE standard 1547-2018 suggests that distributed energy resources (DERs) should provide grid services such as voltage and frequency supports. On the other hand, dynamic events caused by IBRs such as sub-synchronous oscillation have been reported. These developments necessitate improvement in the current modeling capabilities to better understand the interdependencies within power systems. These include interactions between transmission and distribution systems, among IBRs themselves, and between IBRs and conventional resources. In this paper, we present a co-simulation model integrating PSS/E, OpenDSS, and PSCAD to analyze IBR impacts on the stability of transmission and distribution systems. A key challenge in developing a co-simulation model is ensuring interoperability among different simulators (interfacing and data flow) while maintaining accurate results. Using the developed model, we simulate the impact of IBRs on power systems in two test cases: 1) fault ride through (FRT) capability during a generation trip contingency; 2) IBR-induced sub-synchronous oscillation. The results show the effectiveness of the co-simulation model.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Early Alarm: Robust Event Analysis for Power Systems using 1-D Fully Convolutional Network

This work presents a novel deep learning model for early, accurate, and robust detection, recognition, and temporal localization of multi-type events in large-scale power systems. The proposed method develops a unified 1-D fully convolutional network (FCN) model that takes time series of raw frequency signals measured from a power system as input, extracts distinguishing features, and predicts at every temporal point in the time series if an event is happening and what the type of the event is. Compared to existing methods, the proposed model eliminates the necessity for hand-crafted feature extraction or complicated data pre-processing, can flexibly handle input signals of arbitrary length, and precisely infer the event occurrence time. Most importantly, the model is intentionally trained with incomplete patterns, such that it is more robust to partial features of an event which is common in real-world online recognition, resulting in early alarm for power system failures. Extensive experimental results demonstrate that the proposed method achieves superior performance to the state-of-the-art, and also shows strong robustness to noise and system oscillations.

Li, Chengcheng↗

Beyond Capacity Credits: Adaptive Stress Period Planning for Evolving Power Systems

This paper combines and applies concepts from several researchers to outline an alternative framework to plan power systems for resource adequacy needs, which we call Adaptive Stress Period Planning (ASPP). It first provides background information regarding least-cost planning objectives and the challenge of balancing an increasing need for model representation with computational intensity as power systems evolve in complexity. Next, it motivates the opportunity for a new paradigm by outlining challenges of frameworks in use today that rely on aggregate capacity heuristics (i.e., capacity credits and planning reserve margins). Subsequently, it lays out main process details of ASPP, which more directly represents spatial and temporal dynamics of power systems in a capacity expansion model with a process to adaptively select risk periods. The paper concludes with a summary of the approach, its benefits, and opportunities for future work.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

High-Performance Computing Based EMT Simulation of Large PV or Hybrid PV Plants

Faults in the transmission grid have led to reduced power generation from power electronics resources that are typically not connected to the faulted transmission line. In many of the cases, partial loss of power is observed within the power electronics resources like large photovoltaic (PV) power plants. This phenomena is not captured in existing simulation models and/or simulators. High-fidelity switched system electromagnetic transient (EMT) dynamic models of PV power plants can improve the fidelity of models available for accurate analysis of the impact on PV plants during simulation of faults. However, these models are extremely computationally expensive and take a long time to simulate. Long simulation times limit the ability to use these models as larger regions are studied in EMT simulations with more power electronics resources. In this paper, numerical simulation algorithms are combined with high-performance computing techniques and applied to the high-fidelity switched system EMT model of PV plants. Using these techniques, a speed-up of up to 58x is obtained, while preserving the accuracy of the simulation at greater than 98%.

Debnath, Suman↗

Mathematical Models for Optimization of Grid-Integrated Energy Storage Systems

Energy storage has been proven to yield positive effects on planning, operation and control of electric grids. It has become a crucial task to properly model the energy storage systems (ESS) under the framework of grid optimization on transmission and distribution networks including microgrids. This paper presents a review on mathematical models and test cases of ESSs used for grid optimization studies, where the network constraints of power systems are included. The existing ESS models are mainly classified into two categories – linear and nonlinear models. The two main categories are further divided into several subcategories respectively; such as mixed-integer linear and convex nonlinear sub-categories. Based on the review and discussions, this paper aims at providing suggestions for choosing proper ESS models for specific grid optimization studies considering the chosen power network model.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Dynamic systems modeling of the spallation neutron source cryogenic moderator system to optimize transient control and prepare for power upgrades

Through support of the US Department of Energy's Office of Basic Energy Sciences, Oak Ridge National Laboratory has begun applying machine learning methods to improve accelerator and target performance of the Spallation Neutron Source (SNS). These methods are being applied to the control optimization and power upgrade of the SNS Cryogenic Moderator System (CMS). A numerical model of the CMS has been developed to study these optimizations and system modifications using EcosimPro. This paper compares steady-state and transient numerical results with experimental data. Control optimization studies focused on dampening mass flow, temperature, and pressure fluctuations during sudden losses of accelerator beam power. This analysis was conducted by adjusting five proportional-integral-derivative controllers connected to four flow control valves and one heater. Future efforts include power uprate studies focused on increasing the CMS cooling capacity. The current accelerator power is 1.4 MW; the first target station is being upgraded to 2.0 MW as part of the Proton Power Upgrade effort. The CMS cooling capacity is sufficient for 2.0 MW operation.

47 OTHER INSTRUMENTATION↗

Dynamic-Phasor Model of Transformer Inrush Simulation for Unbalanced Distribution System

Abstract— Power system stability is a critical concern, with respect to the increasing distributed energy resources, microgrids, and emphasis on enhancing resiliency. For stability analysis and evaluation, modeling and simulation capabilities are important. In this paper, the authors’ previous work on modeling transformer inrush dynamics is improved with a new formulation of the key equation that converts the dynamic-phasor into real-time value. Simulation results in an electromechanical time-scale are compared with PSCAD results in an electromagnetic time-scale to demonstrate the improvement. The impact of simulation step size is discussed. A bug in PSCAD transformer setting that leads to incorrect magnetizing flux and current under Delta winding type is identified and reported. In addition, explanations and analysis are provided on two modeling techniques utilized since the previous work. This work is being incorporated into the next release of GridLAB-D. The improved transformer inrush calculation enables more accurate predictions of current amplitudes for analyzing the dynamics of distribution operation and control.

power transformers, power system simulation, satur↗

Identifying Robust Decarbonization Pathways for the Western U.S. Electric Power System Under Deep Climate Uncertainty

Climate change threatens the resource adequacy of future power systems. Existing research and practice lack frameworks for identifying decarbonization pathways that are robust to climate-related uncertainty. We create such an analytical framework, then use it to assess the robustness of alternative pathways to achieving 60% emissions reductions from 2022 levels by 2040 for the Western U.S. power system. Our framework integrates power system planning and resource adequacy models with 100 climate realizations from a large climate ensemble. Climate realizations drive electricity demand; thermal plant availability; and wind, solar, and hydropower generation. Among five initial decarbonization pathways, all exhibit modest to significant resource adequacy failures under climate realizations in 2040, but certain pathways experience significantly less resource adequacy failures at little additional cost relative to other pathways. By identifying and planning for an extreme climate realization that drives the largest resource adequacy failures across our pathways, we produce a new decarbonization pathway that has no resource adequacy failures under any climate realizations. This new pathway is roughly 5% more expensive than other pathways due to greater capacity investment, and shifts investment from wind to solar and natural gas generators. Our analysis suggests modest increases in investment costs can add significant robustness against climate change in decarbonizing power systems. Our framework can help power system planners adapt to climate change by stress testing future plans to potential climate realizations, and offers a unique bridge between energy system and climate modeling.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DRE: Designing for Resilience through Emulation

Threats to cyber- and cyber-physical systems have continued to increase over the past decade. Now more than ever, it is vital that cyber-physical system stakeholders have the tools to deeply understand their systems and the threats they face. High-fidelity modeling capabilities are powerful tools to support system understanding and decision-making, but they currently lack scientifically rigorous experimentation practices and infrastructures. The purpose of the project Designing for Resilience through Emulation (DRE) was to bring together past research and existing tools into a new pipeline to improve our ability to quantitatively evaluate future cyber scenarios. DRE offers new integrated capabilities for large dataset collection, noise studies, sensitivity analysis, uncertainty quantification, and surrogate modeling using a cyber-physical system emulation environment. These new capabilities significantly improve our ability to establish the credibility of the answers derived from emulation environments, ultimately leading to better critical decision support.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗