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At least 253 records · Page 14

Large Scale Bilevel Optimization for N-K SCOPF Using Adversarial Robustness

Ensuring a secure dispatch against multiple simultaneous outages has long been desired to maintain grid security in the presence of severe events, such as extreme weather phenomena. Traditionally denoted as N-k security constrained optimal power flow (N-k SCOPF), this problem is intractable to solve due to its size being combinatorial in the number of simultaneous outages and due to the non-convex nature of the AC network constraints. This hinders the use of N-k SCOPF for operating realistic-scale systems. In this paper, we introduce a methodology to scalably solve an AC-feasible dispatch that improves security over k simultaneous outages. Our methodology poses N-k SCOPF as a bilevel optimization problem and solves it using an adversarial robustness approach. We develop new efficient methods to solve each level of the bilevel optimization by employing knowledge of the physics of the underlying system. This yields significant improvements in speed and convergence that enable us to address the N-k SCOPF problem at scale. We demonstrate the effectiveness of our method by conducting a comprehensive analysis of an N-3 SCOPF for a 500-bus network. Furthermore, we emphasize the ability of our physics-driven techniques to handle larger systems by successfully scaling up to 12,000 buses.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Spatial-Temporal PV Hosting Capacity Estimation and Evaluation

Evaluating Photovoltaic Hosting Capacity (PVHC) is an essential step in the process of integrating solar energy into power grids, particularly when focusing on the distribution network (DN) as the primary integration target. PVHC needs to be investigated, especially in cases where the grids are unbalanced, and their operational conditions vary spatially and temporally. This motivation prompted us to propose a scalable model tailored to this application. In this paper, we applied linearization to the alternating current optimal power flow (AC-OPF) and solar inverters, transforming the original problem into a mixed-integer linear programming (MILP) problem. Additionally, we accounted for the battery energy storage system (BESS) as a time-coupling factor for calculating PVHC. We then compared the PVHC results between the IEEE-13 bus and SMART-DS San Francisco (SFO) cases and discussed the extent to which BESS can enhance the PVHC of a DN. Furthermore, we designed a web-based graphical visualization for the SFO case, enabling user interaction with raw data and simulation results on a map through a graphical user interface (GUI). In summary, our results and findings provide valuable insights for future three-phase unbalanced AC-OPF PVHC practices and their visualization.

AC-optimal power flow↗

Recent advances and challenges in optimization models for expansion planning of power systems and reliability optimization

Optimization models for expansion planning of power systems aim to determine capacities, investment timing, and location of power systems to satisfy the power demands while minimizing the total cost. The models have become complex in recent years to reflect both regulations on conventional energy sources and the increasing penetration of renewable energy sources (RES). This paper reviews the basic concepts and optimization models for expansion planning of power systems. We first explain the definition and features of generation expansion planning (GEP), transmission expansion planning (TEP), and generation and transmission expansion planning (GTEP). To address the computational challenges of large-scale expansion planning problems, we review several simplifications including temporal and spatial aggregation, and decomposition methods. This paper also addresses power system reliability defined as the probability of satisfying the load demand while withstanding failures of components. Finally, the goal of this paper is to provide a research overview, discuss trends in expansion planning of power systems, and suggest directions for future research.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Peak Load Management in Distribution Systems Using Legacy Utility Equipment and Distributed Energy Resources

The ability to perform peak load management in distribution systems has several benefits for utilities, including reduced demand charges and improved reliability, efficiency, and utilization of the network infrastructure. This paper demonstrates the coordinated operation of an advanced distribution management system (ADMS) and a distributed energy resource management system (DERMS) to achieve peak load management using a realistic laboratory test bed. A commercial ADMS reduces the peak demand by reducing system voltages using a dynamic voltage regulation (DVR) application. A prototype DERMS-based on real-time optimal power flow-controls distributed battery energy storage systems to further reduce the feeder power. Results from the experiments conducted using a model of a real distribution feeder show that the coordinated operation of the ADMS and DERMS is effective in accomplishing peak load management.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Conservation Voltage Reduction with Distributed Energy Resource Management System, Grid-Edge, and Legacy Devices

Distribution utilities use conservation voltage reduction (CVR) to obtain energy savings and lower peak demand by reducing bus voltages. Traditionally, the CVR is accomplished by controlling the legacy assets such as load tap changers, voltage regulators, and capacitor banks. The deployment of the advanced distribution management system (ADMS) and distributed energy resource management system (DERMS) enables the integration of distributed energy resources into the distribution networks and provide the grid services including CVR. This paper studies the coordinated operation of an ADMS and a DERMS in achieving CVR and voltage regulation. A commercial ADMS uses legacy devices and Edge-of-Network Grid Optimization (ENGO) devices to obtain energy savings through CVR. A prototype DERMS dispatches the photovoltaic smart inverters based on real-time optimal power low to ensure voltage regulation across the feeder. The results show that the coordinated operation of ADMS and DERMS is effective in achieving CVR and voltage regulation. Specifically, energy savings of up to 4.7% are observed in the real utility distribution system used in this study.

advanced distribution management system↗

Peak Load Management in Distribution Systems Using Legacy Utility Equipment and Distributed Energy Resources: Preprint

The ability to perform peak load management in distribution systems has several benefits for utilities, including reduced demand charges and improved reliability, efficiency, and utilization of the network infrastructure. This paper demonstrates the coordinated operation of an advanced distribution management system (ADMS) and a distributed energy resource management system (DERMS) to achieve peak load management using a realistic laboratory test bed. A commercial ADMS reduces the peak demand by reducing system voltages using a dynamic voltage regulation (DVR) application. A prototype DERMS—based on real-time optimal power flow—controls distributed battery energy storage systems to further reduce the feeder power. Results from the experiments conducted using a model of a real distribution feeder show that the coordinated operation of the ADMS and DERMS is effective in accomplishing peak load management.

61 RADIATION PROTECTION AND DOSIMETRY↗

WEC-Sim Modeling of Laminar Scientific's Patented Seesaw Wave Energy Converter: Preprint

Laminar Scientific's patented seesaw wave energy converter was modeled in WEC-Sim to predict performance. The device operates by utilizing ocean surface waves to rotate a truss in pitch about a pivot. The pivot is located at the top of two pylons, which are embedded in the seafloor. The seesaw has a float on either end, and the buoyancy forces from each float cause the system to rise or fall with passing waves. Device performance relies upon seesaw length and ocean wavelength creating an antiphase effect. The seesaw truss has an adjustable length intended to achieve this effect. The operation method enforces a narrow band of wavelengths which induce the largest rotational motion from the device. The hydrodynamic analysis of the device was performed using Capytaine, and the results confirmed that the device operates best in a narrow frequency band. Four float-to-float spacing cases and three pylon radii were examined. The hydrodynamic results indicate a match between the model and the physical expectations for the device, and that varying the pylon radii by 0.1-m increments for three instances creates minimal changes in hydrodynamic properties. Power matrices for three float spacing cases of the device were simulated with Joint North Sea Wave Project spectra waves and optimal power take-off damping in WEC-Sim. The maximum average power production for the 15-m spacing case was 14.1 kW with a 5.0-s peak wave period and 4-m significant wave heights. Plots of capture-width ratios indicated that the device performance was linear and confirmed that the device is optimal in a narrow frequency band. The maximum percentage of the available wave power produced by the 15-m device was approximately 16%. Simulations of the device in regular waves were used to produce plots of average power compared to a ratio of float spacing to wavelength. These plots indicate that the power production is maximized at a ratio of 0.5, and further confirm that the device has a narrow frequency response. The device was simulated at an example field location, where the device produced an annual average power rating of 1.6 kW given an average omnidirectional wave climate of 10.3 kW m-1 and an optimal, linearized power take-off model. While the maximum predicted device performance is reliant upon a narrow band of wave frequencies, the conducted analysis provides an opportunity to improve device design prior to prototyping and testing. Modifying the design to respond to a broader frequency range would improve device performance.

numerical modeling↗

Conservation Voltage Reduction with Distributed Energy Resource Management System, Grid-Edge, and Legacy Devices

Distribution utilities use conservation voltage reduction (CVR) to obtain energy savings and lower peak demand by reducing bus voltages. Traditionally, the CVR is accomplished by controlling the legacy assets such as load tap changers, voltage regulators, and capacitor banks. The deployment of the advanced distribution management system (ADMS) and distributed energy resource management system (DERMS) enables the integration of distributed energy resources into the distribution networks and provide the grid services including CVR. This paper studies the coordinated operation of an ADMS and a DERMS in achieving CVR and voltage regulation. A commercial ADMS uses legacy devices and Edge-of-Network Grid Optimization (ENGO) devices to obtain energy savings through CVR. A prototype DERMS dispatches the photovoltaic smart inverters based on real-time optimal power low to ensure voltage regulation across the feeder. The results show that the coordinated operation of ADMS and DERMS is effective in achieving CVR and voltage regulation. Specifically, energy savings of up to 4.7% are observed in the real utility distribution system used in this study.

advanced distribution management system↗

Conservation Voltage Reduction with Distributed Energy Resource Management System, Grid-Edge, and Legacy Devices

Distribution utilities use conservation voltage reduction (CVR) to obtain energy savings and lower peak demand by reducing bus voltages. Traditionally, the CVR is accomplished by controlling the legacy assets such as load tap changers, voltage regulators, and capacitor banks. The deployment of the advanced distribution management system (ADMS) and distributed energy resource management system (DERMS) enables the integration of distributed energy resources into the distribution networks and provide the grid services including CVR. This paper studies the coordinated operation of an ADMS and a DERMS in achieving CVR and voltage regulation. A commercial ADMS uses legacy devices and Edge-of-Network Grid Optimization (ENGO) devices to obtain energy savings through CVR. A prototype DERMS dispatches the photovoltaic smart inverters based on real-time optimal power low to ensure voltage regulation across the feeder. The results show that the coordinated operation of ADMS and DERMS is effective in achieving CVR and voltage regulation. Specifically, energy savings of up to 4.7% are observed in the real utility distribution system used in this study.

advanced distribution management system↗

Conservation Voltage Reduction with Distributed Energy Resource Management System, Grid-Edge, and Legacy Devices: Preprint

Distribution utilities use conservation voltage reduction (CVR) to obtain energy savings and lower peak demand by reducing bus voltages. Traditionally, the CVR is accomplished by controlling the legacy assets such as load tap changers, voltage regulators, and capacitor banks. The deployment of the advanced distribution management system (ADMS) and distributed energy resource management system (DERMS) enables the integration of distributed energy resources into the distribution networks and provide the grid services including CVR. This paper studies the coordinated operation of an ADMS and a DERMS in achieving CVR and voltage regulation. A commercial ADMS uses legacy devices and Edge-of-Network Grid Optimization (ENGO) devices to obtain energy savings through CVR. A prototype DERMS dispatches the photovoltaic smart inverters based on real-time optimal power low to ensure voltage regulation across the feeder. The results show that the coordinated operation of ADMS and DERMS is effective in achieving CVR and voltage regulation. Specifically, energy savings of up to 4.7% are observed in the real utility distribution system used in this study.

advanced distribution management system↗

Feasible region-based heuristics for optimal transmission switching

In this paper, we develop a optimal transmission switching (OTS) heuristic based on DC optimal power flow (OPF) and assess the efficacy of the approach when implemented within AC OPF. Traditional formulations of the OTS problem can result in hundreds or thousands of binary variables for large networks, making the OTS problem challenging to solve on fast timescales even for relatively small networks. Here, we identify which constraints and therefore which variables are constraining the DC OPF feasible region, and rank them based on their impact on the cost function. We develop a heuristic algorithm which iteratively removes these constraints and solves a series of standard DC OPF problems. The heuristic is tested on a variety of PGlib networks and the results show that the algorithm can provide substantial cost decreases without having to solve any mixed integer programs. Additionally, we provide insights about the OTS problem, including identifying scenarios outside congestion where OTS can prove useful. Lastly, the performance of the DC-based heuristic is shown when the line switching decisions are implemented within AC OPF.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

ARPA-E Grid Optimization (GO) Competition Challenge 1

The ARPA-E Grid Optimization (GO) Competition Challenge 1, from 2018 to 2019, focused on the basic Security Constrained AC Optimal Power Flow problem (SCOPF) for a single time period. The Challenge utilized sets of unique datasets generated by the ARPA-E GRID DATA program. Each dataset consisted of a collection of power system network models of different sizes with associated operating scenarios (snapshots in time defining instantaneous power demand, renewable generation, generator and line availability, etc.). The datasets were of two types: Real-Time, which included starting-point information, and Online, which did not. Week-Ahead data is also provided for some cases but was not used in the Competition. Although most datasets were synthetic and generated by GRIDDATA, a few came from industry and were only used in the Final Event. All synthetic Input Data and Team Results for the GO Competition Challenge 1 for the Sandbox, Trial Events 1 to 3, and the Final Event along with problem, format, scoring and rules descriptions are available here. Data for industry scenarios will not be made public. Challenge 1, a minimization problem, required two computational steps. Solver 1 or Code 1 solved the base SCOPF problem under a strict wall clock time limit, as would be the case in industry, and reported the base case operating point as output, which was used to compute the Objective Function value that was used as the scenario score. The feasibility of the solution was provided by the Solver 2 or Code 2, which solves the power flow problem for all contingencies based on the results from Solver 1. This is not normally done in industry, so the time limits were relaxed. In fact, there were no time limits for Trial Event 1. This proved to be a mistake, with some codes running for more than 90 hours, and a time limit of 2 seconds per contingency was imposed for all other events. Entrants were free to use their own Solver 2 or use an open-source version provided by the Competition. Containers, such as Docker, were considered to improve the portability of codes, but none that could reliably support a multi-node parallel computing environment, e.g., MPI, could be found. For more information on the competition and challenge see the "GO Competition Challenge 1 Information" and "GO Competition Challenge 1 Additional Information" resources below.

ACOPF↗

PowerGridworld: A Framework for Multi-Agent Reinforcement Learning in Power Systems [SWR-22-07]

NREL's PowerGridworld provides a modular simulation environment for training heterogenous, grid-aware, multi-agent reinforcement learning (RL) policies at scale. The package enables the user to create component gym environments that can be composed into more complex agents. For example, a grid interactive building environment can be created by composing together component environments each encapsulating the building, PV, and battery physics. These multi-component environments can then be combined into multi-agent simulation where each agent's power consumption/injection becomes an input for solving the optimal power flow on a distribution feeder modeled in OpenDSS. Information from OpenDSS, such as bus voltages and line flows, can be included in the agents' observation spaces to enable grid-aware rewards. The default API for the PowerGridworld simulator conforms to RLLib's MultiAgent API and thus enables distributed training using HPC and cloud resources.

Biagioni, David↗

Learning Local Volt/VAR Controllers Toward Efficient Network Operation with Stability Guarantees: Preprint

This paper considers the problem of voltage regulation in distribution network. The primary motivation is to keep voltages within pre-assigned operating limits by commanding the reactive power output of distributed energy resources (DERs) deployed in the grid. We develop a framework for developing local Volt/Var control that comprises of two main steps. In the first, exploiting historical data and for each DER, we learn a function representing desirable equilibrium points for the power network. These points approximate solutions of an Optimal Power Flow problem. In the second, we propose a control scheme for steering the network towards these favorable configurations. Theoretical conditions are derived to formally guarantee the stability of the developed control scheme and numerical simulations illustrate the effectiveness of the proposed approach.

data-driven control↗

Trajectory Design Employing Convex Optimization for Landing on Irregularly Shaped Asteroids

Mission proposals that land on asteroids are becoming popular. However, in order to have a successful mission the spacecraft must reliably and softly land at the intended landing site. The problem under investigation is how to design a fuel-optimal powered descent trajectory that can be quickly computed on- board the spacecraft, without interaction from ground control. An optimal trajectory designed immediately prior to the descent burn has many advantages. These advantages include the ability to use the actual vehicle starting state as the initial condition in the trajectory design and the ease of updating the landing target site if the original landing site is no longer viable. For long trajectories, the trajectory can be updated periodically by a redesign of the optimal trajectory based on current vehicle conditions to improve the guidance performance. One of the key drivers for being completely autonomous is the infrequent and delayed communication between ground control and the vehicle. Challenges that arise from designing an asteroid powered descent trajectory include complicated nonlinear gravity fields, small rotating bodies and low thrust vehicles. There are two previous studies that form the background to the current investigation. The first set looked in-depth at applying convex optimization to a powered descent trajectory on Mars with promising results.1, 2 This showed that the powered descent equations of motion can be relaxed and formed into a convex optimization problem and that the optimal solution of the relaxed problem is indeed a feasible solution to the original problem. This analysis used a constant gravity field. The second area applied a successive solution process to formulate a second order cone program that designs rendezvous and proximity operations trajectories.3, 4 These trajectories included a Newtonian gravity model. The equivalence of the solutions between the relaxed and the original problem is theoretically established. The proposed solution for designing the asteroid powered descent trajectory is to use convex optimization, a gravity model with higher fidelity than Newtonian, and an iterative solution process to design the fuel optimal trajectory. The solution to the convex optimization problem is the thrust profile, magnitude and direction, that will yield the minimum fuel trajectory for a soft landing at the target site, subject to various mission and operational constraints. The equations of motion are formulated in a rotating coordinate system and includes a high fidelity gravity model. The vehicle's thrust magnitude can vary between maximum and minimum bounds during the burn. Also, constraints are included to ensure that the vehicle does not run out of propellant, or go below the asteroid's surface, and any vehicle pointing requirements. The equations of motion are discretized and propagated with the trapezoidal rule in order to produce equality constraints for the optimization problem. These equality constraints allow the optimization algorithm to solve the entire problem, without including a propagator inside the optimization algorithm.

Pinson, Robin M.↗

Machine Learning-Assisted Distribution System Network Reconfiguration Problem

High penetration from volatile renewable energy resources in the grid and the varying nature of loads raise the need for frequent line switching to ensure the efficient operation of electrical distribution networks. Operators must ensure maximum load delivery, reduced losses, and the operation between voltage limits. However, computations to decide the optimal feeder configuration are often computationally expensive and intractable, making it unfavorable for real-time operations. This is mainly due to the existence of binary variables in the network reconfiguration optimization problem. To tackle this issue, we have devised an approach that leverages machine learning techniques to reshape distribution networks featuring multiple substations. This involves predicting the substation responsible for serving each part of the network. Hence, it leaves simple and more tractable Optimal Power Flow problems to be solved. This method can produce accurate results in a significantly faster time, as demonstrated using the IEEE 37-bus distribution feeder. Compared to the traditional optimization-based approaches, a feasible solution is achieved approximately ten times faster for all the tested scenarios.

deep neural networks↗

Modeling the AC Power Flow Equations with Optimally Compact Neural Networks: Application to Unit Commitment

Nonlinear power flow constraints render a variety of power system optimization problems computationally intractable. Emerging research shows, however, that the nonlinear AC power flow equations can be successfully modeled using neural networks. These neural networks can be exactly transformed into mixed integer linear programs and embedded inside challenging optimization problems, thus replacing nonlinearities that are intractable for many applications with tractable piecewise linear approximations. Such approaches, though, suffer from an explosion of the number of binary variables needed to represent the neural network. Accordingly, this paper develops a technique for training an "optimally compact'' neural network, i.e., one that can represent the power flow equations with a sufficiently high degree of accuracy while still maintaining a tractable number of binary variables. We demonstrate the use of this neural network as an approximator of the nonlinear power flow equations by embedding it in the AC unit commitment problem, transforming the problem from a mixed integer nonlinear program into a more manageable mixed integer linear program. We use the 14-, 57-, and 89-bus networks as test cases and compare the AC-feasibility of commitment decisions resulting from the neural network, DC, and linearized power flow approximations. Our results show that the neural network model outperforms both the DC and linearized power flow approximations when embedded in the unit commitment problem. The neural network formulation most often selects a feasible unit commitment schedule, and furthermore, it only s

AC power flow↗