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At least 37 records · Page 2

Decentralized Voltage optimization Method for Distribution Networks with Distributed PVs

In order to solve the voltage violations caused by high penetration distributed photovoltaics (PVs), this paper proposes a decentralized voltage optimization method for distribution networks. Through the optimal dispatch of distributed PVs and reactive power resources, it realizes the voltage security and economic operation of distribution systems and facilitates the integration of massive distributed PVs. The proposed method is inspired by the idea of locational marginal price (LMP) and feasible cutting plane. It separates the optimization of boundary bus voltage and power flow in adjacent regions and uses the interaction of boundary variables’ marginal prices to obtain the optimal solution. The effectiveness and accuracy of the proposed method are demonstrated via simulation test on a practical l0kV feeder in China.

Xue, Yaosuo↗

Cybersecurity Anomaly Detection in SCADA-Assisted OT Networks Using Ensemble-Based State Prediction Model

The cybersecurity threats of power system gradually grow due to the increased sophisticated interactions between Information Technology (IT) and Operational Technology (OT) networks. False data injection attack (FDIA) that aims to compromise the Supervisory Control and Data Acquisition (SCADA) measurement and disturb the system operation is one of such cyber threats. Such attacks can potentially lead to significant operational issues at the control centers and substations, and hence, result in severe physical consequences. To avoid catastrophic failure across the power grid resulting from these attacks, it is essential to arm the OT network with real-time vulnerability assessment tools. To this end, this paper outlines various drawbacks of the Purdue architecture model to defend against cyberattacks in the OT network. Furthermore, a novel ensemble-based state prediction model is proposed to detect cybersecurity anomalies in SCADA assisted OT networks. The proposed model uses control center level generation and load forecasts, scheduled, and forced outages, power flow solutions, and the substation level historical data. The hypothesis of the proposed scheme relies on the fact that additional control center and substation data can hardly be accessed and compromised by attackers. One of the vital features of the proposed scheme is an hour-ahead prediction of the operational feasibility of the SCADA measurement range at the control center and substation in real time helps in detecting anomalies in measurements across both substation and the control center.

24 POWER TRANSMISSION AND DISTRIBUTION↗

OPF-Learn: An Open-Source Framework for Creating Representative AC Optimal Power Flow Datasets: Preprint

Increasing levels of renewable generation motivate a growing interest in data-driven approaches for AC optimal power flow (AC OPF) to manage uncertainty. However, a lack of disciplined dataset creation and benchmarking prohibits useful comparison between approaches in the literature. To instigate confidence, models must be able to reliably predict solutions across a wide range of operating conditions. This paper develops the OPF-Learn package for Julia and Python which uses a computationally efficient approach to create representative datasets that span a wide spectrum of the AC OPF feasible region. Load profiles are uniformly sampled from a convex set that contains the AC OPF feasible set. For each infeasible point found, the convex set is reduced using infeasibility certificates, found by utilizing properties of a relaxed formulation. The framework is shown to generate datasets which are more representative of the entire feasible space versus traditional techniques seen in the literature, improving machine learning model performance.

dataset↗

Tuning successive linear programming to solve AC optimal power flow problem for large networks

Successive linear programming (SLP) is a practical approach for solving large-scale nonlinear optimization problems. Alternating current optimal power flow (ACOPF) is no exception, particularly the large size of real-world networks. However, in order to achieve tractability, it is essential to tune the SLP algorithm presented in the literature. This paper presents a modified SLP algorithm to solve the ACOPF problem, specified by the U.S. Department of Energy’s (DOE) Grid Optimization (GO) Competition Challenge 1, within strict time limits. The algorithm first finds a near-optimal solution for the relaxed problem (i.e., Stage 1). Then, it finds a feasible solution in the proximity of the near-optimal solution (i.e., Stage 2 and Stage 3). The numerical experiments on test cases ranging from 500-bus to 30,000-bus systems show that the algorithm is tractable. Here the results show that our proposed algorithm is tractable and can solve more than 80% of test cases faster than the well-known Interior Point Method while significantly reduce the number of iterations required to solve ACOPF. The number of iterations is considered an important factor in the examination of tractability which can drastically reduce the computational time required within each iteration.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DNN-based policies for stochastic AC OPF

We report a prominent challenge to the safe and optimal operation of the modern power grid arises due to growing uncertainties in loads and renewables. Stochastic optimal power flow (SOPF) formulations provide a mechanism to handle these uncertainties by computing dispatch decisions and control policies that maintain feasibility under uncertainty. Most SOPF formulations consider simple control policies such as affine policies that are mathematically simple and resemble many policies used in current practice. Motivated by the efficacy of machine learning (ML) algorithms and the potential benefits of general control policies for cost and constraint enforcement, we put forth a deep neural network (DNN)-based policy that predicts the generator dispatch decisions in real time in response to uncertainty. The weights of the DNN are learnt using stochastic primal–dual updates that solve the SOPF without the need for prior generation of training labels and can explicitly account for the feasibility constraints in the SOPF. The advantages of the DNN policy over simpler policies and their efficacy in enforcing safety limits and producing near optimal solutions are demonstrated in the context of a chance constrained formulation on a number of test cases.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Mobile Sorption-based Thermal Battery for Harvesting Low-Temperature Geothermal Energy

Around 20% of the total primary energy in the United States is consumed for thermal demands of buildings such as space cooling, dehumidification, and space heating (EIA 2018). Low-temperature geothermal energy is abundant and can effectively satisfy buildings’ thermal demands. However, low-temperature geothermal energy is underutilized because the energy density of geothermal fluid is too low to justify the costs associated with transporting it between existing geothermal resources and buildings. The mobile sorption-based thermal battery (MSTB) system has been developed using three-phase (i.e., vapor–liquid, solution–solid, crystal) sorption technology to harvest low-temperature heat and store it with a much higher energy density than the geothermal fluid. The energy density of salt crystals is over six times higher than geothermal fluid, which makes long-distance transportation of salt crystals economically feasible. Salt crystals can be used to dehumidify air or provide space cooling in buildings, which alleviates peak demand on the electricity grid by offsetting electricity use for these end uses. This helps improve the grid’s stability and resilience. High-energy storage density, fast crystallization, and dissolution of salt crystals are all critical to the viability and performance of the MSTB system. Therefore, the design and operation of MSTB systems need to ensure effective generation and dissolution of salt crystals inside the MSTB. To achieve this target, this seedling project developed an experimental apparatus for characterizing the crystallization and dissolution processes. The energy density and potential latent cooling capacity of the MSTB are also evaluated based on lab test results. The crystallization results showed that the generated lithium chloride hydrate crystals are fluffy, the crystallization process lasts about 50 min, and the maximum crystal fraction (i.e., the ratio of crystal mass to the mass in the MSTB) can be up to 51.1% of the total mass in the MSTB at a solution flow rate of 1.58 g/s. The dissolution results show that the salt crystals in the MSTB can be fully dissolved within 15–28 min, based on different test conditions. Reducing solution flow rate and cooling water temperature can achieve increased energy storage density and crystal fraction. While the increase in the discharge rate (i.e., latent cooling capacity for dehumidifying air) is achieved by increasing flow rate and temperature of inlet diluted solution, as well as by using a pump for internal solution circulation, the discharge rate increases by 38%, from 0.95 kW to 1.31 kW. Compared with increasing the inlet solution flow rate, power consumption of salt solution transportation can be reduced by using a pump for internal solution circulation. The crystallization test results also showed that the maximum energy storage density is 981.8 kJ/kg, and the maximum discharge rate of the dissolution tests is ≤1.79 kW. Both are above the target values of 900 kJ/kg and 1.75 kW) for this project. The work reported here proves the feasibility and advancement of the MSTB system, which is helpful to the further study and improvement of the MSTB system.

15 GEOTHERMAL ENERGY↗

Integration of a Concentrating Solar Steam Topping Turbine to an Existing Geothermal Binary Power Plant (CRADA CRD-17-700 Final Report)

U.S. Geothermal Inc. is a publicly traded leading renewable energy company focused on the development, production, and sale of electricity from geothermal energy with operating generation facilities at Raft River, Idaho; San Emidio, Nevada; and Neal Hot Springs, Oregon. (U.S. Geothermal was acquired by Ormat Technologies, Inc. since the execution of this project). This project will evaluate the integration of a concentrating solar steam topping cycle with a geothermal bottoming binary cycle. This hybrid plant configuration will enable high efficiency conversion of the solar heat to electrical power, while simultaneously increasing the power output of the bottoming cycle. Such a design has the potential to reduce the cost of electrical power from concentrating solar power (CSP) and geothermal plants, and a viable design could be deployed at existing geothermal plants that have experienced resource productivity decline as well as in greenfield projects. This analysis will examine US Geothermal’s Raft River power plant for implementation of this concept. INL and NREL have previously collaborated in the analysis of geo-solar hybrid power plants. These two labs will again work together to model the solar field, steam-topping, and geothermal bottoming cycles in order to investigate the performance of the proposed cycle. This analysis will provide US Geothermal with technical information including equipment specifications and estimated power generation necessary to advance the concept toward implementation. A successful project outcome has two parts: One would be for US Geothermal to have a process flow diagram (heat and mass balance), and performance parameters for the major pieces of equipment. US Geothermal could then use this information evaluate the techno-economic feasibility of the hybrid power cycle, and serve as the basis for subsequent detailed equipment design, purchase, and installation of the cycle. The second would be for the developed solution to be published by DOE for use by other geothermal developers both to retrofit underperforming plants and to lower the LCOE of new developments.

15 GEOTHERMAL ENERGY↗

Tightest Mixed-Integer Programming Formulations for Quadratic SCUC Optimization

In this project, we developed new, tighter Mixed-Integer Programming (MIP) formulations for the combined Alternating Current (AC) Security-Constrained Unit Commitment (SCUC) and Security-Constrained Optimal Power Flow (SCOPF). The work addresses a critical challenge in power system operations: efficiently determining which generation units to commit and how to optimally dispatch them while maintaining network reliability constraints for both normal and contingency scenarios. Our efforts: 1. Advance the Understanding of SCUC/SCOPF Modeling: By introducing tighter MIP formulations and leveraging cutting-edge optimization tools (Julia/JuMP, PowerModels.jl), this project has pushed forward the state of the art in efficient power systems scheduling. 2. Enhance Technical and Economic Feasibility: The methods developed provide more accurate and potentially faster solutions to large-scale, realistic scheduling and dispatch problems in electric power systems, which can translate into improved reliability and potentially lower costs for grid operations. 3. Benefit to the Public: Greater efficiency in power system operations leads to cost savings for utilities and end-users. Improved reliability and integration of advanced modeling approaches can facilitate the adoption of clean energy resources and better accommodate uncertainties in renewable generation. Because this technology could impact bulk power markets and reliability, these innovations have far-reaching public benefits in terms of cost savings, reliability, and sustainability.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Intelligent System Partitioning for Agent-Based Security Constrained Optimal Power Flow

This project developed scalable, computationally efficient algorithms to solve realistic large-scale power system optimization problems as part of a larger series of competitions run by ARPA-E. These problems are important because the secure and reliable operation of the power grid, especially under increased uncertainty and variability, is growing increasingly challenging. The economic feasibility of the proposed methods developed by our team is quite low, considering it’s a purely software-based solution to operate power grids more efficiently. The technical effectiveness, as evidenced by our performance in the competition, balances heuristics and approximations to provide a tradeoff between speed and accuracy.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Hierarchical Distributed Optimal Power Flow of HV and MV Distribution Networks With Continuous and Discrete Devices

With large-scale distributed photovoltaics (PVs) being integrated into distribution networks (DNs), coordinated optimal power flow (OPF) of high voltage (HV) and medium voltage (MV) DNs should be investigated to optimally dispatch the distributed PVs and other network devices. Here, this paper presents a hierarchical distributed OPF method for HV and MV DNs with on-load tap changers, reactive power compensators, feeder switches and distributed PVs. A hierarchical master-slave control architecture is applied to implement coordinated OPF of two-layer DNs. The HV master problem and MV subproblems are transformed into mixed-integer convex problems respectively with second order cone programming and LinDistFlow approximation. Since there is no efficient distributed algorithm to solve such OPF models with integer subproblems, a novel distributed algorithm is proposed in this paper to efficiently solve the hierarchical coordinated OPF model with integer subproblems in a distributed manner. In the proposed algorithm, the coordinated OPF model is solved in a branch-and-bound framework, where in each branch node generalized Benders decomposition (GBD) algorithm is applied to decompose the coordinated OPF model into a master problem and relaxed subproblems and solves them iteratively to get optimal solution. The GBD optimal and feasible cutting planes generated in a branch node are proved to be valid for its descendants. Moreover, three acceleration techniques are introduced into the proposed algorithm to improve computational efficiency. Finally, the effectiveness and accuracy of the proposed method are verified via simulation tests in Jinzhai DNs of China.

42 ENGINEERING↗

Stochastic AC optimal power flow: A data-driven approach

There is an emerging need for efficient solutions to stochastic AC Optimal Power Flow (AC-OPF) to ensure optimal and reliable grid operations in the presence of increasing demand and generation uncertainty. Herein this paper presents a highly scalable data-driven algorithm for stochastic AC-OPF that has extremely low sample requirement. The novelty behind the algorithm’s performance involves an iterative scenario design approach that merges information regarding constraint violations in the system with data-driven sparse regression. Compared to conventional methods with random scenario sampling, our approach is able to provide feasible operating points for realistic systems with much lower sample requirements. Furthermore, multiple sub-tasks in our approach can be easily paralleled and based on historical data to enhance its performance and application. We demonstrate the computational improvements of our approach through simulations on different test cases in the IEEE PES PGLib-OPF benchmark library.

42 ENGINEERING↗

DERMS Online: A New Voltage Sensitivity-Enabled Feedback Optimization Framework

This paper proposes a distributed energy resource management system (DERMS) solution by developing a new voltage sensitivity-enabled feedback optimization framework. The key idea is to adopt a measurement feedback scheme to reformulate the original nonlinear optimization into a linear programming (LP) problem via perturb-and-observe-based voltage sensitivity analysis. The proposed solution eliminates the dependence on load knowledge and can be implemented online thanks to an efficient open-source solver for LP problems. Comparison results with other control methods on a real distribution feeder in Southern California highlight the feasibility as well as benefits for the proposed framework.

distributed energy resource management↗

Electrochemical Control for Corrosion in Molten Chlorides During CSP Plant Operation

The Liquid Pathway of the Concentrating Solar Power Generation 3 (CSP Gen3) program proposed low-cost molten chloride salt for energy storage. However, online corrosion control was identified as a remining major risk of the Liquid Pathway approach. This project addressed that risk. Electrochemical solutions for corrosion mitigation during CSP plant operation were investigated and their feasibility and scalability were evaluated. The leading cause of corrosion in molten chloride salt systems was identified as corrosive impurities that form within the salt upon exposure to trace amounts of air and moisture. Leveraging electrochemistry, reduction/oxidation reactions can be employed to remove these corrosive impurities. In Phase 1 of this project, a bench-scale batch electrochemical reactor was designed, fabricated, and used to assess the kinetics and thermodynamics of electrochemical salt purification. In Phase 2, a laboratory-scale flow reactor was designed, fabricated, and used to assess the efficacy of the electrochemical method under flowing conditions. Results show that under proposed operating conditions for the Liquid Pathway Gen3 Pilot Plant, the electrochemical method is significantly more effective at removing impurities than alternative chemical and thermal methods, and that the electrochemical method produces less harmful byproducts. A key advance made in the course of this project was the development of a 2-electrode method for electrochemical purification that is more scalable than previously developed 3 electrode methods. This novel method is based on Magnesium (Mg) electrowinning. A provisional patent based on this invention has been submitted (USPTO Application No. 63/480,355). Additional key advances made during this project include assessment of the effect of dissimilar alloys on corrosion, kinetic and thermodynamic evaluation of thermolysis reactions of impurities within the molten salt, characterization of byproducts of purification reactions, and generation of IP focused on isolating value-added products using molten salt-based electrochemistry that could be deployed to valorize the process (USPTO Application No. 63/478,806). Ultimately, this project represented a step toward feasibility of Liquid Pathway Gen3 CSP. The method developed under this project could significantly reduce capital expenses and operating costs and increase plant profitability by enabling use of less expensive alloys, decreasing maintenance, and increasing plant longevity. Key focus areas for follow-on work have been identified as 1) evaluation of the efficacy of the electrochemical method under turbulent conditions in a larger flow system, such as the FASTR loop, 2) development of methods for removal of purification byproducts, 3) modeling pilot and industrial scale performance of electrochemical salt purification during plant operation and 4) further assessment of the effect of impurities on salt vapor phase.

14 SOLAR ENERGY↗

Development of a MOOSE thermal model of the MPC-32 canister and HI-STORM overpack

Nuclear power is a significant source of electricity in the United States, but the average age of nuclear power plants is around 40 years old. Safe management of spent nuclear fuel (SNF) is a key aspect of the back end of the nuclear fuel cycle, and SNF dry storage systems are becoming a popular, effective solution in this area, given the absence of a final disposal system. The spent fuel cask system (dry cask method) provides a feasible solution for maintaining SNF (~60 years) prior to final disposal. This project aims to develop a thermal model of the MPC-32 canister and HI-STORM overpack, using the Multiphysics Object-Oriented Simulation Environment (MOOSE). MOOSE is an open-source framework developed by Idaho National Laboratory (INL) for multiscale, multiphysics simulations. This study will investigate and demonstrate the thermal-hydraulics capabilities of the MOOSE framework, including natural circulation, heat transfer, porous flows, etc. The ultimate goal of the project is to verify whether MOOSE tools (including Pronghorn) can be used to study the thermal performance of the SNF dry cask storage system. This study provides reliable and inclusive solving strategy for dry cask problems. The detailed information about the solving scheme and the governing equations related to the physics of the system is provided in the report. The results for thermal-hydraulic analysis of the HI-STORM system is produced with using open source modules of the MOOSE framework. This results highlights the flexibility and modularity of the MOOSE which makes it a unique candidate for the frameworks and code packages. Therefore, integration of the MOOSE to UNF ST&DARDS will improve the thermal-hydraulic capability of the system while providing distinctive features to users.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Non-linear dynamics of jet quenching

We develop a comprehensive analytic framework for jet quenching in QCD media, based on a medium-induced parton cascade sourced by collinear virtual splittings. We show that the energy flow out of the jet cone, driven by turbulent gluon cascades, is governed by a non-linear rate equation that resums gluon splittings at arbitrary angles and is enhanced by the medium length, L. The solution of this equation sets the initial condition for a non-linear DGLAP-like evolution equation, which describes the collinear early vacuum cascade resolved by the medium at angles exceeding the medium resolution angle, θ c . For asymptotic jet energies, the medium-induced cascade displays an exponential behavior that generalizes the Poisson-like distribution of parton energy loss. This formulation enables the resummation of leading contributions in α s ln(1/R), and α s ln(R/θ c ), and powers of α s L. We briefly explore the limit of strong quenching, where analytic treatments are feasible, offering insights into the impact of parton cascades on jet quenching. These results provide guidance for future numerical simulations and analytical investigations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Energy-Storing Cryogenic Carbon Capture™ for Utility and Industrial-scale Processes (Final Report)

This project leverages work completed under funding from previous U.S. Department of Energy (DOE) projects and other sources of funding around the Cryogenic Carbon Capture Process™ (CCC). The purpose of this project is to further develop a perturbation of the CCC process that stores energy called the Cryogenic Carbon Capture™ Energy Storing (CCC-ES) process. The successful completion of the tasks in this project has prepared the CCC-ES process for scaling and developed initial plans and a feasibility analysis for an engineering-scale or small-commercial scale system that can store at least 10 MWh of energy. The specific areas of work in this project are: (1) Develop a conceptual, site-specific integrated process flow diagram: Sustainable Energy Solutions (SES) developed a conceptual, site-specific plan including a process flow diagram for integrating CCC-ES at a specific location. The proposed system was based on a typical engineering-scale CCC system designed to capture 30-50 metric tonnes of CO 2 per day (TPD), with modifications and additional changes and equipment put in place to add the energy storage element to the system. The 30-50 TPD scale corresponds to the required 10 MWh of stored energy. This task includes updating simulation software for transient analysis. SES also coordinated with the Jim Bridger Power Plant in Point of Rocks, Wyoming to get site-specific data. Using these data, SES completed site-specific preliminary process flow diagrams, estimated capital and operating costs for the CCC-ES system, and used the upgraded software package to estimate the primary figures of merit. (2) Complete a technoeconomic analysis (TEA): SES completed a full-scale TEA of the CCC-ES system to meet specific success criteria. This TEA used process information from the host site selected above. (3) Technology and data gap assessment for the CCC-ES process: As part of the technology gap assessment, SES performed an assessment of the best alternative energy storage solutions including figures of merit and operational strengths and weaknesses. SES then compared the proposed system with a discussion of how it addresses the weaknesses of alternative systems, showing for example a cost of stored energy at less than $50/MWh and with a roundtrip efficiency of greater than 95%. This assessment includes technical and other risk analyses and technology gaps with research and development needs to commercialize the process by 2030. Finally, work includes a commercialization roadmap/development pathway outlining the major milestones to move to a commercially operating CCC-ES system. (4) Phase II pre-FEED project plan: SES completed a project plan for the Phase II pre-FEED analysis using the same host site as in the Phase I conceptual study.

03 NATURAL GAS↗

Development of an innovative process for post-combustion CO 2 capture to produce high-value NaHCO 3 nanomaterials

An innovative post-combustion process using aqueous sodium glycinates solutions (SGS) for CO 2 capture from a split flue gas stream emitted from the 600 MWe coal power plant, used in the Wolverine Clean Energy Venture (WCEV) project, was developed in Aspen Plus v.10. The flue gas flow rate used in the process was 12.43 kg/s (at 353.15 K and 101.33 kPa) and contained 0.0023 and 13.33 mol% of SO 2 and CO 2 , respectively. The overall process includes 5 main units designed to remove all SO 2 and capture more than 90 mol% of CO 2 in the flue gas stream, while producing high-value, salable sodium bicarbonate (NaHCO 3 ) nanomaterials to offset the total process costs. The hydraulics, mass transfer characteristics, and process performance obtained using Aspen Plus were discussed. Also, the capital expenditure (CAPEX), operating expenditure (OPEX), and Levelized cost of CO 2 capture (LCOC) were calculated to assess the feasibility of this process. Further, the hydraulics in the SO 2 washing and CO 2 capture units showed a pressure drop of 12 and 1 kPa, respectively, and the behaviors of the liquid holdup and normalized packing specific wetted surface area were similar in both units. The gas-side mass transfer coefficients were orders of magnitude greater than the liquid-side mass transfer coefficients. The process was able to capture 2.352 kg/s of CO 2 and produce 4.486 kg/s of valuable NaHCO 3 nanomaterials. Also, the calculated CAPEX, OPEX, and LCOC of the process for a 30-year plant lifetime were ($\$ $4,450,552), (233.00 $\$ $/h) and (35.49 $\$ $/ton of CO 2 captured), respectively.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DERMS Online: A New Voltage Sensitivity-Enabled Feedback Optimization Framework: Preprint

This paper proposes a distributed energy resource management system (DERMS) solution by developing a new voltage sensitivity enabled feedback optimization framework. The key idea is to adopt a measurement feedback scheme to reformulate the original nonlinear optimization into a linear programming (LP) problem via perturb and observe-based voltage sensitivity analysis. The proposed solution eliminates the dependence on load knowledge and can be implemented online thanks to an efficient open-source solver for LP problems. The proposed DERMS online platform is generalizable to deal with various types of distributed energy resources (DERs), including distributed photovoltaics (PVs), energy storage, electric vehicles, demand response, etc. Comparison results with other control methods on a realistic distribution feeder in Southern California highlight the feasibility as well as benefits for the proposed framework.

distributed energy resources management↗