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Data systems - Optical bus will connect distributed system
The fiber optics buses currently under consideration for the NASA space station program will have data rates of at least 20 Mb/sec. The network interconnection scheme that will eventually be used is, however, uncertain. A NASA-Langley effort on graph networks includes adaptive, fault-tolerant and self-correcting operations. A breadboard program at NASA-Johnson includes evaluations of standard devices, called 'bus interface units', in an adaptive and fault-correcting chordal or concentric ring type of network. Fiber optics would allow an evolution to wavelength modulation techniques in such systems.
Optimal dynamic control of resources in a distributed system
The authors quantitatively formulate the problem of controlling resources in a distributed system so as to optimize a reward function and derive optimal control strategies using Markov decision theory. The control variables treated are quite general; they could be control decisions related to system configuration, repair, diagnostics, files, or data. Two algorithms for resource control in distributed systems are derived for time-invariant and periodic environments, respectively. A detailed example to demonstrate the power and usefulness of the approach is provided.
A comparative study of electric power distribution systems for spacecraft
The electric power distribution systems for spacecraft are compared concentrating on two interrelated issues: the choice between dc and high frequency ac, and the converter/inverter topology to be used at the power source. The relative merits of dc and ac distribution are discussed. Specific converter and inverter topologies are identified and analyzed in detail for the purpose of detailed comparison. Finally, specific topologies are recommended for use in dc and ac systems.
Multi-Fidelity Gaussian Process for Distribution System Voltage Probabilistic Estimation with PVs
The increasing penetration of behind-the-meter PVs causes challenges to maintain voltage security due to the lack of distribution system visibility. This paper proposes a nonlinear autoregressive Gaussian process (NARGP) approach to fuse limited number of SCADA/AMI data together with historical pseudo measurements for distribution node voltage probabilistic estimation. The high-fidelity SCADA data are fused with the low-fidelity AMI and pseudo measurements by the autoregressive algorithm embedded in the Gaussian process. This allows us to use multi-fidelity data to achieve entire distribution system voltage visibility. Numerical results carried out on the IEEE 123node system demonstrate that the NARGP method is able to obtain high accuracy in estimating bus voltage and quantifying estimation uncertainties as compared to other approaches.
Environmental control and air distribution system and method of using the same
An indoor environmental control and air distribution system for a building includes: an air handling unit; a manifold connected to the air handling unit having a chamber formed by a plurality of walls and a plurality of orifices formed through at least one of the walls; air distribution conduits each independently having a first end connected to the orifices of the manifold and a second end extending out from the manifold into different zones throughout the building; and an airflow modulating device having one or more airflow regulating dampers independently configured to move into at least two positions in which each position provides a different percentage of total air volume to each air distribution conduit. A method of using the indoor environmental control and air distribution system is also included.
Environmental control and air distribution system and method of using the same
An indoor environmental control and air distribution system for a building includes: an air handling unit; a manifold connected to the air handling unit having a chamber formed by a plurality of walls and a plurality of orifices formed through at least one of the walls; air distribution conduits each independently having a first end connected to the orifices of the manifold and a second end extending out from the manifold into different zones throughout the building; and an airflow modulating device having one or more airflow regulating dampers independently configured to move into at least two positions in which each position provides a different percentage of total air volume to each air distribution conduit. A method of using the indoor environmental control and air distribution system is also included.
Application of Autonomous Smart Inverter Volt-VAR Function for Voltage Reduction Energy Savings and Power Quality in Electric Distribution Systems: Preprint
This paper evaluated the impact of smart inverter Volt-VAR function on voltage reduction energy saving and power quality in electric power distribution systems. A methodology to implement the voltage reduction optimization was developed by controlling the substation LTC and capacitor banks, and having smart inverters participate through their autonomous Volt-VAR control. In addition, a power quality scoring methodology was proposed and utilized to quantify the effect on power distribution system power quality. All of these methodologies were applied to a utility distribution system model to evaluate the voltage reduction energy saving and power quality under various PV penetrations and smart inverter densities.
Fault tolerant distributed systems using Ada
This paper discusses the use of Ada on distributed systems in which failure of processors has to be tolerated. It is assumed that communication between tasks on separate processors will take place using the facilities of the Ada language, primarily the rendezvous. It is shown that there are numerous aspects of the language which make its use on a distributed system very difficult. The issues are raised from the desire to be able to recover, reconfigure, and provide continued service in the presence of hardware failure. For example, if a rendezvous takes place between two tasks on different processors, failure of the processor executing the serving task will cause the calling task to be permanently suspended because the rendezvous will never end. Extensive modifications to the execution support required for Ada are proposed which provide all the necessary facilities for programs written in Ada to withstand arbitrary processor failure. Mechanisms are suggested to allow processor failure to be detected and for tasks which would be permanently suspended to be released. Provided the required program structures are used, continued processing can be provided.
Building a generalized distributed system model
A modeling tool for both analysis and design of distributed systems is discussed. Since many research institutions have access to networks of workstations, the researchers decided to build a tool running on top of the workstations to function as a prototype as well as a distributed simulator for a computing system. The effects of system modeling on performance prediction in distributed systems and the effect of static locking and deadlocks on the performance predictions of distributed transactions are also discussed. While the probability of deadlock is considerably small, its effects on performance could be significant.
Multi Time-scale Imputation aided State Estimation in Distribution System
With the transition to a smart grid, we are witnessing a significant growth in sensor deployments and smart metering infrastructure in the distribution system. However, information from these sensors and meters are typically unevenly sampled at different time-scales and are incomplete. It is critical to effectively aggregate these information sources for situational awareness. In order to reconcile the heterogeneous multi-scale time-series data, we present a multi-task Gaussian process framework. This framework exploits the spatio-temporal correlation across the time-series data to impute data at any desired timescale while providing confidence bounds on the imputations. The value of the imputed data for distribution system operation is illustrated via a matrix completion based state estimation strategy. Results on the IEEE 37 bus distribution system reveals the superior performance of the proposed approach relative to linear interpolation approaches.
Deep Reinforcement Learning for Distribution System Operations: A Tutorial and Survey
Here, the rapid evolution of modern electric power distribution systems into complex networks of interconnected active devices, distributed generation (DG), and storage poses increasing difficulties for system operators. The large-scale integration of distributed energy resources (DERs) and the rapid exchange of measurement data via communication networks present major opportunities for advancing grid operations but also introduce greater uncertainty, higher data dimensionality, more complex network and device models, and challenging control and optimization problems. Deep reinforcement learning (DRL) algorithms are promising in addressing these challenges. However, they have not been effectively adapted for power systems applications, requiring extensive customization for implementation and evaluation. This has resulted in reproducibility challenges and a steep learning curve for researchers new to applying DRL algorithms to the power systems domain. To bridge these gaps, this tutorial aims to serve as a valuable resource for researchers interested in exploring learning-based algorithms to operate active power distribution networks. Specifically, this work presents a generalized process for translating sequential decision-making problems in power distribution systems into Markov decision process (MDP) formulations, illustrated through concrete grid service examples. Additionally, we introduce a simple environment design strategy to develop and evaluate example DRL algorithms for distribution system applications, complete with an included code repository to guide users through environment construction.
Decision-Dependent Uncertainty-Aware Distribution System Planning Under Wildfire Risk
The interaction between power systems and wildfires can be dangerous and costly. Distribution grids can be liable for the outbreak of wildfires during extreme weather. In wildfire-prone areas, investment planning should consider the impact of operational actions on wildfire-related uncertainties affecting line failure likelihood. Here, in this case, endogenous-based uncertainty modeling should comprise the backbone of the investment planning model viz-a-viz the inability of standard exogenous-based uncertainty modeling. Therefore, we propose a decision-dependent uncertainty (DDU) aware methodology to optimize investment portfolios for distribution systems, considering that high power-flow levels in high-threat areas can ignite wildfires and increase line failure probability. The methodology identifies the best combination of upgrades (new lines, hardening existing lines, and placing switching devices). Methodologically, we propose a two-stage distributionally robust planning optimization problem with DDU that considers the distribution system's multiperiod operation. The first stage determines optimal switching actions and line investments, and the second stage evaluates the worst-case expected operational cost under a DDU framework designed to account for the endogenous impact of power-flow levels and hardening investment decisions in the line failure probabilities. An iterative method is tailored to handle the problem and numerical experiments demonstrate a more prepared grid to deal with wildfire risk.
Distribution System Congestion Management - A Survey of Reliable Integration for Aggregated Resources and Microgrids
Rising penetration of consumer-owned Distribution Grid Resources (DGRs), increasingly managed by third party aggregators and enrolled in grid services and wholesale market programs, can create localized congestion in distribution networks. Managing these constraints is challenging due to a persistent coordination and information gap: utilities are accountable for reliability and have network topology and state visibility, while aggregators control the DGR capability needed to relieve congestion. This survey synthesizes congestion management solutions for distribution systems with high DGR penetration, covering both market-based mechanisms (distribution level markets, locational pricing, flexibility auctions) and non-market-based solutions (network reconfiguration, direct DGR control, demand response, curtailment, etc.). The literature is organized across three decision horizons: long term planning, operational planning, and real-time operation. Special attention is devoted to emerging distribution system operator architectures and coordination frameworks spanning transmission system operators, aggregators, and microgrids. Drawing on recent case studies and implementations, we distill best practices, identify key technical and economic barriers, and outline research directions. The evidence points to a shift toward integrated congestion management that combines market signals with technical controls, enabled by improved monitoring, forecasting, and closed loop control capabilities.
Data-Driven Global Sensitivity Analysis of Three-Phase Distribution System with PVs
Global sensitivity analysis (GSA) of distribution system with respect to stochastic PV variations plays an important role in designing optimal voltage control schemes. This paper proposes a Kriging, i.e., Gaussian process modeling enabled data-driven GSA method. The key idea is to develop a surrogate model that captures the hidden global relationship between voltage and real and reactive power injections from the historical data. With the surrogate model, the Sobol index can be conveniently calculated to assess the global sensitivity of voltage to various power injection variations. Comparison results with other model-based GSA methods on the IEEE 37-bus feeder, such as the polynomial chaos expansion and the Monte Carlo approaches demonstrate that the proposed method can achieve accurate GSA outcomes while maintaining high computational efficiency.
A two-stage stochastic optimization model for scheduling electric vehicle charging loads to relieve distribution-system constraints
Electric vehicles (EVs) hold promise to improve the energy efficiency and environmental impacts of transportation. However, widespread EV use can impose significant stress on electricity-distribution systems due to their added charging loads. This paper proposes a centralized EV charging-control model, which schedules the charging of EVs that have flexibility. This flexibility stems from EVs that are parked at the charging station for a longer duration of time than is needed to fully recharge the battery. The model is formulated as a two-stage stochastic optimization problem. The model captures the use of distributed energy resources and uncertainties around EV arrival times and charging demands upon arrival, non-EV loads on the distribution system, energy prices, and availability of energy from the distributed energy resources. We use a Monte Carlo-based sample-average approximation technique and an L-shaped method to solve the resulting optimization problem efficiently. We also apply a sequential sampling technique to dynamically determine the optimal size of the randomly sampled scenario tree to give a solution with a desired quality at minimal computational cost. Here, we demonstrate the use of our model on a Central-Ohio-based case study. We show the benefits of the model in reducing charging costs, negative impacts on the distribution system, and unserved EV-charging demand compared to simpler heuristics. Lastly, we also conduct sensitivity analyses, to show how the model performs and the resulting costs and load profiles when the design of the station or EV-usage parameters are changed.
Optimization Framework to Assess the Demand Response Capacity of a Water Distribution System
As large electricity consumers, water distribution system (WDS) pumping stations have the potential to become meaningful participants in demand response (DR) programs. The authors propose an optimization framework for assessing the DR capacity of a WDS and identifying the optimal bidding strategy for maximizing WDS revenue in the DR spot market. The proposed mixed integer linear programming (MILP) model overcomes computational constraints of previous DR optimization models by adopting a preprocessing procedure to minimize the number of binary variables and implementing a convex relaxation technique to linearize the hydraulic equations. The proposed MILP model also explicitly accounts for varying levels of risk tolerance of WDS operators by varying the recovery period over which pumping returns to business-as-usual operation. The optimization framework is implemented on a skeletonized 48-node WDS model that includes 7 pumps, 6 tanks, and 39 pipes. Using a simulated DR event and water consumption profile, the authors derive the optimal DR supply curves (i.e., compensation price versus load curtailment quantity) and revenue potential of the WDS under six scenarios for DR participation.
Multiperiod Distribution System Restoration With Routing Repair Crews, Mobile Electric Vehicles, and Soft-Open-Point Networked Microgrids
This paper proposes a distribution system restoration model which is in response to multiple outages caused by natural disasters. The proposed restoration model includes the coordination of routing repair crews (RRCs), mobile batterycarried vehicles (MBCVs), and networked microgrids (NMGs) formed by soft open points (SOPs). The travel and repair time constraints are modeled for each RRC; travel path and charging strategy are modeled for each MBCV; and the network reconfiguration is developed considering the optimal operation of SOP-based NMGs. Furthermore, the proposed model is presented as a mixedinteger linear program which is solved by an auxiliary induce function based algorithm to reduce the computational complexity. The modified IEEE 33-bus and 69-bus distribution systems are tested with multiple outages. The presented results demonstrate the effectiveness of the proposed model