Distributed optimal power flow and the multi-agent system for the realization in cyber-physical system
Not Available
Nguyen, Tung-Lam↗
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Results for “power system optimization”
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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Optimized silicon solar cells for space exploration power systems
A program is described aimed at designing and fabricating improved silicon solar cells for a range of missions extending from 0.1 to 15 astronomical units (Mercury to Jupiter). For missions lying inside Earth radius (Mercury, Venus) the major cell property required was very low series resistance, allowing high curve fill factor to be maintained at the higher intensities. For the Mercury mission, the temperature of the cell had to be kept low. This was achieved by reflecting more of the incident sunlight by use of large area front contacts. For the outer missions (Mars, the Asteroid belts and Jupiter) the formation of a Schottky barrier at the back contact had to be avoided (by use of a P+ layer under the back contact) and the excess leakage current of the PN junction had to be reduced. Optimum grid patterns were also derived and used for these missions.
Iles, P. A.↗
An Iterative Response-Surface-Based Approach for Chance-Constrained AC Optimal Power Flow Considering Dependent Uncertainty
A modern power system is characterized by a stochastic variation of the loads and an increasing penetration of renewable energy generation, which results in large uncertainties in its states. These uncertainties bring formidable challenges to the power system planning and operation process. To address these challenges, we propose a cost-effective, iterative response-surface-based approach for the chance-constrained AC optimal power-flow problem that aims to ensure the secure operation of the power systems considering dependent uncertainties. Starting from a stochastic-sampling-based framework, we first utilize the copula theory to simulate the dependence among multivariate uncertain inputs. Then, to reduce the prohibitive computational time required in the traditional Monte-Carlo method, we propose, instead of using the original complicated power-system model, to rely on a polynomial-chaos-based response surface. This response surface allows us to efficiently evaluate the time-consuming power-system model at arbitrary distributed sampled values with a negligible computational cost. This further enables us to efficiently conduct an online stochastic testing for the system states that not only screens out the statistical active constraints, but also assists in a better design of the tightened bounds without using any Gaussian or symmetric assumption. Finally, an iterative procedure is executed to fine-tune the optimal solution that better satisfies a predefined probability. The simulations conducted in multiple test systems demonstrate the excellent performance of the proposed method.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Error-Level-Controlled Synthetic Forecasts for Renewable Generation
Renewable energy resources, including solar and wind energy, play a significant role in sustainable energy systems. However, the inherent uncertainty and intermittency of renewable generation pose challenges to the safe and efficient operation of power systems. Recognizing the importance of short-term (hours ahead) renewable generation forecasting in power systems operation, it becomes crucial to address the potential inaccuracies in these forecasts. To systematically evaluate the performance of controllers in the presence of imperfect forecasts, we generate synthetic forecasts using actual renewable generation profiles (one from solar and one from wind). These synthetic forecasts incorporate different levels of statistical error, allowing us to control and manipulate the accuracy of the predictions. The primary objective is to employ synthetic forecasts with controlled yet realistic error levels to systematically investigate how controllers adapt to variations in forecast accuracy, providing valuable insights into their robustness and effectiveness under real-world conditions.
Array↗
Coordinated voltage control and reactive power regulation between transmission and distribution systems
Systems and methods are described for coordinating volt-var control between sub-transmission and distribution systems. Distributed energy resources of a distribution system are aggregated into virtual power plants from which reactive power can optimally be dispatched to the sub-transmission system. A sub-transmission controller executes a volt-var AC optimal power flow optimisation function to minimize voltage fluctuations that might otherwise occur when coordinating with a distribution system having distributed energy resources. The distribution system can use a sensitivity matrix for regulating voltage at distribution feeders while fulfilling a transmission or sub-transmission system's demand requests.
Samaan, Nader A.↗
State Estimation-Based Distributed Energy Resource Optimization for Distribution Voltage Regulation in Telemetry-Sparse Environments Using a Real-Time Digital Twin
Real-time state estimation using a digital twin can overcome the lack of in-field measurements inside an electric feeder to optimize grid services provided by distributed energy resources (DERs). Optimal reactive power control of DERs can be used to mitigate distribution system voltage violations caused by increased penetrations of photovoltaic (PV) systems. In this work, a new technology called the Programmable Distribution Resource Open Management Optimization System (ProDROMOS) issued optimized DER reactive power setpoints based-on results from a particle swarm optimization (PSO) algorithm wrapped around OpenDSS time-series feeder simulations. This paper demonstrates the use of the ProDROMOS in a RT simulated environment using a power hardware-in-the-loop PV inverter and in a field demonstration, using a 678 kW PV system in Grafton (MA, USA). The primary contribution of the work is demonstrating a RT digital twin effectively provides state estimation pseudo-measurements that can be used to optimize DER operations for distribution voltage regulation.
Darbali-Zamora, Rachid↗
Linear Approximation Line Pack Model for Integrated Electricity and Natural Gas Systems OPF
With the increasing installation of renewable energy electricity generation capacity, the penetration of natural gas fired generation is also substantailly increasing in power systems due to their fast response capability to mitigate system variability and uncertainty. The dependencies between the electricity system and the natural gas system are becoming more important to maintain the reliability of both systems. In integrated electricity and natural gas systems (IEGSs) operations, the nonlinearity of the natural gas system brings challenges to the system operators. In this paper, the IEGSs optimal power flow (OPF) model is proposed that includes the line pack of the gas system via linear approximation. The simulation results demonstrate that the proposed model can obtain optimal solutions efficiently that are comparable in accuracy as the nonlinear line pack model.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Resilience Enhancements through Deep Learning Yields
This report documents the Resilience Enhancements through Deep Learning Yields (REDLY) project, a three-year effort to improve electrical grid resilience by developing scalable methods for system operators to protect the grid against threats leading to interrupted service or physical damage. The computational complexity and uncertain nature of current real-world contingency analysis presents significant barriers to automated, real-time monitoring. While there has been a significant push to explore the use of accurate, high-performance machine learning (ML) model surrogates to address this gap, their reliability is unclear when deployed in high-consequence applications such as power grid systems. Contemporary optimization techniques used to validate surrogate performance can exploit ML model prediction errors, which necessitates the verification of worst-case performance for the models.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Performance Optimization of the Gasdynamic Mirror Propulsion System
Nuclear fusion appears to be a most promising concept for producing extremely high specific impulse rocket engines. Engines such as these would effectively open up the solar system to human exploration and would virtually eliminate launch window restrictions. A preliminary vehicle sizing and mission study was performed based on the conceptual design of a Gasdynamic Mirror (GDM) fusion propulsion system. This study indicated that the potential specific impulse for this engine is approximately 142,000 sec. with about 22,100 N of thrust using a deuterium-tritium fuel cycle. The engine weight inclusive of the power conversion system was optimized around an allowable engine mass of 1500 Mg assuming advanced superconducting magnets and a Field Reversed Configuration (FRC) end plug at the mirrors. The vehicle habitat, lander, and structural weights are based on a NASA Mars mission study which assumes the use of nuclear thermal propulsion' Several manned missions to various planets were analyzed to determine fuel requirements and launch windows. For all fusion propulsion cases studied, the fuel weight remained a minor component of the total system weight regardless of when the missions commenced. In other words, the use of fusion propulsion virtually eliminates all mission window constraints and effectively allows unlimited manned exploration of the entire solar system. It also mitigates the need to have a large space infrastructure which would be required to support the transfer of massive amounts of fuel and supplies to lower a performing spacecraft.
Emrich, William J., Jr.↗
Toward a scalable robust security-constrained optimal power flow using a proximal projection bundle method
Robust security-constrained optimal power flow (rSCOPF) aims to find the worst-case contingencies of alternating current optimal power flow (ACOPF) in power systems. With the rise of GPU architectures on the upcoming supercomputer architectures, optimization algorithms that rely on sparse linear algebra and indefinite linear systems are becoming increasingly hard to solve efficiently (e.g. interior-point method). To address this we revisit a maximin optimization formulation of the rSCOPF and the single-level mixed-integer semidefinite programming (MISDP) reformulation, which is obtained by taking the Lagrangian relaxation of the inner minimization ACOPF problem. In this paper, we focus on the development of a proximal projection bundle method (PPBM) for solving continuous relaxation node subproblems of the MISDP problem, based primarily on the well-known alternating direction method of multipliers. Cutting planes reminiscent of bundle method ideas are also applied in coordination with updates of the proximal parameter. The cutting-plane method can generate a large number of linear inequalities, leading to a large scale but decomposable quadratic programming (QP) subproblem that is amenable to GPUs. We present the numerical results on the IEEE 30, 57, 118, and 300-bus systems by using our PBMM method. We discuss the main computational bottleneck of our method, which is the time taken to solve each iteration of a QP subproblem instance of the PPBM, and how GPU architectures can accelerate this solution process.
bundle method↗
Cost-optimal wave-powered persistent oceanographic observation
Not Available
Dillon, Trent (ORCID:0000000201036449)↗
Postdisaster Routing of Movable Energy Resources for Enhanced Distribution System Resilience: A Deep Reinforcement Learning-Based Approach
The deployment of movable energy resources (MERs) can be an effective strategy to restore critical loads to enhance power system resilience when no other energy sources are available after the occurrence of an extreme event. Since the optimal locations of MERs following an extreme event are dependent on system operating states (e.g., the loads at each node, on/off status of system branches, and so on), existing analytical and population-based approaches must repeat the entire analysis and calculation when the system operating states change. On the contrary, if deep reinforcement learning (DRL)-based algorithms are sufficiently trained with a wide range of scenarios, they can quickly find optimal or near-optimal locations irrespective of changes in system states. A deep Q-learning-based approach is proposed for optimal MER deployment to enhance power system resilience. MERs can be also utilized to complement other types of resources, if available. The proposed approach operates in two stages after the occurrence of extreme events. In the first stage, the distribution network is represented as a graph, and the network is then reconfigured using tie switches by using Kruskal’s spanning forest search algorithm (KSFSA). To maximize critical load recovery, the optimal or near-optimal locations of MERs are chosen in the second stage. Further, case studies on a 33-node distribution system and a modified IEEE 123-node system demonstrate the effectiveness of the proposed approach for postdisaster routing of MERs.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Optimization of Dimensions of Smooth and Twisted-Tape-Inserted Tubes for Heat Transfer with NaCl/KCl/MgCl 2 Molten Salts by Principle of Entropy Generation Minimization
The entropy generation minimization principle is used as the criterion to optimize the flow and heat transfer of solar collectors and heat exchangers that use molten salts NaCl–KCl–MgCl 2 and KCl–MgCl 2 . The Gnielinski correlation for the Nusselt number versus Reynolds number, as well as the Moody friction factor given by Petukhov, was used for the calculation of the convective heat transfer coefficient and pressure loss due to friction in smooth tubes. For twisted-tap-inserted tube, equations of Nu and friction factor provided by Manglik and Bergles were used. The objective function, the entropy generation rate of the heat transfer system, was expressed as the function of Reynolds number, Prandtl number, heating flux, tube diameter, etc. As a result of the analysis, the optimum Reynolds number was determined and thereby to determine the optimum Nusselt number, convective heat transfer coefficient, friction factor, and tube diameter, which also allows the calculation of optimum flow velocity. The analysis was conducted in the fluid temperature range of 500–700 °C, which covers the operation temperature for supercritical CO 2 power cycles in concentrated solar power (CSP) system. Here, optimized results from the smooth tube and twisted-tap-inserted tube are compared, which is important to the design of solar receivers for CSP systems.
14 SOLAR ENERGY↗
A Clustering-Based Scenario Generation Framework for Power Market Simulation with Wind Integration
A critical step in stochastic optimization models of power system analysis is to select a set of appropriate scenarios and significant numbers of scenario generation methods exist in the literature. This paper develops a clustering based scenario generation method, which aims to improve the performance of existing scenario generation techniques by grouping a set of correlated wind sites into clusters according to their cross-correlations. Copula based models are utilized to model spatiotemporal correlations and the Gibbs sampling is then used to generate scenarios for day-ahead markets. Our results show that the generated scenarios based on clustered wind sites outperform existing approaches in terms of reliability and sharpness and can reduce the total computational time for scenario generation and reduction significantly. The clustering-based framework can therefore provide a better support for real-world market simulations with high wind penetration.
data visualization↗
Cyber-Resilient Frequency Control of Power Grids with Energy Storage Systems
The integration of synchronous generators and energy storage systems operated through communication networks introduces new challenges and vulnerabilities to the electric grid, where cyber attacks can corrupt sensor measurements or control inputs and interrupt functions such as frequency regulation. This paper proposes a defense methodology for the design of resilient operating constraints imposed on each generation and storage unit in order to prevent any attack sequence from driving the system's frequency to unsafe conditions. The resilient operating constraints are found by using ellipsoidal approximations of the reachable set of the power system, leading to a convex optimization problem with linear matrix inequalities. Numerical results in a single-area power system with synchronous generation and energy storage demonstrate how the resilient constraints provide security guarantees against any type of attack affecting frequency measurements or controller setpoints.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Community-Centric Microgrid Feasibility Analysis Framework to Accelerate Resilience of Island Communities
Marine energy offers a reliable energy solution for island and coastal communities, which often lack traditional local generation, to support their transition to energy independence and reduce reliance on externally imported fuels. Successful deployment of new technologies in these isolated locations requires community acceptance and approval from the outset, as these communities typically lack the financial and technical resources to operate and maintain new systems. This report presents a community-centric microgrid planning framework for remote coastal and island communities. Community engagement is integrated as the first step in the planning process, incorporating community profiles and visions into energy development scenarios. A case study was conducted in St. George, Pribilof Islands, Alaska, which relies entirely on diesel yet has significant wind and wave energy potential. Community engagement revealed a unique history and current economic status, with an interest in adopting advanced energy technologies despite past failures. Various microgrid configurations were optimized, considering different technologies to meet current and future energy needs while balancing cost and energy resilience. Wave energy converters (WECs) were a key component, integrated with other energy sources using the Xendee optimization tool. The Marine Energy Microgrid Toolkit, developed as part of this work, uses commercial power system analysis tools to optimize and analyze microgrid scenarios. The developed framework and toolkit can be applied to island and coastal communities to enhance resilience and support microgrid deployments. Future enhancements will include incorporating new marine resources, developing dynamic models, and automating the integration of Xendee and PowerFactory simulations.
02 PETROLEUM↗
Optimizing Insulation Design for Transformers in Medium Voltage Power Conversion Systems
Medium-frequency transformers (MFTs) play a crucial role in medium-voltage (MV) solidstate transformer (SST) systems, particularly in extreme fast charging applications. Achieving partial discharge (PD)-free operation while maintaining high power density is a significant challenge due to the high electric field (E-field) stresses inherent in MV applications. This dissertation focuses on the insulation design and optimization of MFTs used in both the main power electronics circuits and auxiliary power supplies. The study begins with an overview of insulation testing methodologies, including high potential tests, basic insulation level tests, and PD tests, which are critical for evaluating MFT insulation reliability. Given the importance of PD-free operation for long-term reliability, particular emphasis is placed on understanding PD mechanisms, including void, corona, and surface discharge, and their mitigation strategies. A high voltage isolated auxiliary power supply is then introduced, utilizing a gapped transformer encapsulated in silicone gel. This design achieves PD-free insulation up to 18 kV RMS while maintaining low coupling capacitance to minimize common-mode current. The proposed solution ensures reliable operation in MV environments and offers a scalable approach for auxiliary power in cascaded SST architectures. To improve MFT insulation in main power conversion circuits, a novel structure is developed using polypropylene sheets and potting compounds to create a void-free air gap, effectively mitigating E-field intensity. A prototype transformer with this insulation structure is built and achieves PD-free operation up to 30 kV RMS. This design is experimentally validated in a resonant converter operating at 46 kW, demonstrating its feasibility for MV SST applications. Further optimization is implemented to enhance MFT performance for dual-active-bridge(DAB) converters by integrating a semiconductive shielding layer within the insulation structure. This shielding layer improves the magnetic coupling coefficient while effectively confining the E-field within high insulation materials, thereby reducing eddy current losses. The optimized MFT achieves PD-free operation at 12.6 kV RMS and is successfully tested in a DAB converter operating at 43 kW, which meets the insulation requirements for a 13.2 kV SST system. This dissertation advances MFT insulation design by introducing and experimentally validating novel approaches that improve high voltage insulation while optimizing magnetic coupling and manufacturability. The proposed insulation structures enable PD-free operation while minimizing insulation material usage and simplifying assembly, making them ideal for high power, high voltage applications.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
The Apollo Lunar Roving Vehicle.
After listing the basic requirements such as operational demands and lunar environmental conditions, characteristic design data of the roving vehicle are given with special attention to the four wheel drive and its control system, the steering system, and the navigational instrumentation. The overall system is optimized in view of total mass, range, mobility, load carrying capability, safety, highest reliability through redundancy, and last but not least, cost and development time. Optimization of the drive and power system has first been evaluated by mathematical models and computational simulation of the vehicle hardware, the wheel/soil interaction, and the lunar terrain profile. The next step to refine optimization and evaluation of the drive, steering, and power system including man/machine interaction has been carried out by the use of a six-degree-of-freedom simulator subjecting the astronauts in their space suits to simulated vehicle motions while driving over a simulated terrain, observable on a cathode ray tube display.
Haeussermann, W.↗