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DOE OSTI2022

ExaSGD: 2022 Kernel Thrust Activities

The Kernel Thrust milestone ADSE22-407 covers the development of device-capable optimization algorithms and solvers technologies required by the ExaSGD project’s software stack in order to solve security-constrained alternating current optimal power flow (SC-ACOPF) problems on emerging exascale architectures. To this extent, in FY22 the main objective of the Kernel Thrust was (i) provide sparse optimization solver that runs efficiently on hardware accelerator devices (i.e., NVIDIA and AMD GPUs) to perform intra-node computations, (ii) strengthen the reliability and increase the performance of the mixed-dense sparse (MDS) solver of HiOp for deployment on the FY22 target architectures, Summit and Crusher, and (iii) increase performance by improving the mathematical algorithm and refining the parallel MPI-based implementation of the coarse-grain parallel solver HiOp-PriDec for capabilities deployment on the FY22 target architectures, Summit and Crusher. This document presents the developments and contributions done by the Kernels Thrust Team in FY22 toward completion of the above-mentioned objectives. These contributions progressed along four main development (sub)thrusts: (1) Design and implementation of a sparse optimization solver for use on hardware accelerators; (2) Improvement of the mathematical algorithm and of the parallel implementation of HiOp-PriDec to ensure readiness and efficient coarse-grain parallelism for FY23 target exascale machine; and (3) Support Software and Application Development Thrusts of the exaSGD project in their deployment of the project’s software stack on AMD- and NVIDIA-based architectures. The development of the sparse optimization solver (thrust 1 above) was new in FY22 and resulted in a new sparse solver in HiOp (available as of version 0.6). The second development thrust was a continuation of the efforts from FY21 and improved the mathematical algorithm and the communication strategy of the HiOp-PriDec solver. The last developement thrust is a large collaborative effort. Namely, the project’s teams from multiple labs (LLNL, PNNL, ORNL, and NREL) performed large-scale demonstration of the ExaSGD software stack, namely the optimization solvers of HiOp interfaced with the modeling front-end ExaGO and the stochastic sampler PowerScenarios. These demonstration efforts solved large-scale instances of the SC-ACOPF challenge problem of medium network sizes (10, 000-bus system) and large number of contingencies on Summit (NVIDIA accelerators) and Crusher (AMD accelerators) systems at ORNL.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Near-Term Reliability and Resilience (NTRR) (Final Report)

The Near-Term Reliability and Resiliency (NTRR) was awarded in December 2020 as an inter-lab project to examine the reliability and resilience of the electricity grid and natural gas transportation availability. The project builds on studies conducted by The North American Electric Reliability Corporation (NERC), the U.S. Department of Energy (DOE), and other non-governmental research and operational focused on reliability and resilience analyses challenges. The research was conceived to address near-term scenarios (within 10 years), when many local and regional policy transitions could begin to impact grid reliability, resilience, and supporting infrastructure availability. To integrate the natural gas interdependency, the team began with the generating capacity and demand projections from the 2020 NERC Long-Term Reliability Assessment and the Bulk Electric System (BES) transmission topologies defined in the Western Electricity Coordinating Council (WECC) Anchor Data Set, Eastern Interconnection Reliability Assessment Group Multi-Regional Modeling Working Group (ERAG/MMWG) Data Set, the team calculated baseline regional power sector gas demands from present electricity delivery year through the end of delivery year 2030/31 by applying security constrained economic dispatch. This demand was compiled along with demand projections for regional residential, commercial, and industrial natural gas demands from the most recent Energy Information Administration (EIA) Annual Energy Outlook Reference Case into Deloitte’s MarketBuilder® North American Gas Model. Through the application of these demands, MarketBuilder® was projected the topology of natural gas flows in the natural gas pipeline network across the interconnected North American system along with regional natural gas prices that may be seen by market participants in future years Additionally, contingencies and sensitivities focused on the built models of the Eastern Interconnection (EI) and Western Interconnection (WI). They address challenges from the following with the outcomes being an identification of performance under the extreme conditions and an identification of potential grid weaknesses that should be addressed to mitigate the reduced performance and improve the resilience and reliability of the specific regions as well as the National Grid: • Weather events including extreme heat, extreme cold, high wind, no wind, wind and solar forecasting errors, and wildfires. • Gas availability, factoring in supply disruption (contractual and physical), seasonal availability constraints, and infrastructure limitations; and • Transmission availability and congestion.

03 NATURAL GAS↗
DOE OSTI2023

SCUC-DER Integration Report: Integrating Distributed Energy Resources (DER) Using Advanced Unit Commitment Models and DER Aggregation Methodologies

Distributed energy resources (DERs) are continuing to grow due to regulatory, policy, and market shifts, and it needs to be ensured that small DERs are given a level playing field with traditional resources. Legacy market processes such as unit-commitment problems were designed for a power grid consisting largely of centralized power plants. In contrast, DERs consist of many small devices with distinct operating characteristics that may or may not be connected at the transmission interconnection points, limiting their visibility to the independent system operators (ISOs) who operate wholesale electricity markets. This report details the development and initial results from a simulation platform that integrates state-of-the-art security-constrained unit commitment software, detailed feeder models, and a DER aggregator model to quantify potential DER integration issues. Quantitative results to date illustrate potential infeasible scheduling solutions from SCUC when the DERA includes aggregations of energy-limited energy storage resources. Likewise, if aggregations are not penalized for dispatch deviations, they may have incentives to deviate from the SCUC-determined resource schedules. Assumptions about the amount of aggregated demand response resources (DRRs) and the ability of DERAs to follow profit incentives have an important impact – DRRs have significant flexibility and can typically feasibly meet their SCUC schedule, but on the other hand, their profit incentives can cause unscheduled increases in load before and after DRR dispatch. Computation time results on the SCUC solver and simulation platform only show a modest increase in SCUC solution time as the number of DERAs is increased, but results to date only reflect the RTS-GMLC test system; results may show more significant solver slowdown in larger transmission systems. The largest contribution to simulation time is attributed to the DERA offer generation method, which is suggested for future improvements.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Computational Algorithms for Unit Commitment with AC Power Flows (Final Report)

Security-constrained unit commitment (SCUC) is a key component in power system operations. When AC power flow constraints are considered in the SCUC model (AC-SCUC), the problem becomes extremely difficult due to its discrete and non-convex nature, as described in “Grid Optimization Competition Challenge 3 Problem Formulation (GOCC)”. There are four main challenges: (i) Discrete decisions regarding unit online/offline status and start-up/shut-down procedures for every single unit. The number of discrete decision variables increases considerably when a system integrates multiple generators; (ii) Configuration-based combined-cycle formulations, and multi-commodity models that include ramping products, spin/non-spin products, and regulation up/down products. The combined-cycle units introduce additional discrete decision variables and auxiliary service products further complicate the model by connecting multi-commodity products’ continuous and discrete variables; (iii) SCUC models with AC power flow constraints are far more complex due to massive bilinear terms in the large-scale nonlinear power balance equations. The nonlinear power balance equations are further complicated by the discrete step control variables of shunts; (iv) N − 1 contingency analysis. The size of the model increases linearly with the number of contingencies considered, greatly increasing the size of the optimization model. Accordingly, there is an emergent need to develop a robust algorithm capable of deriving a high-quality solution in a short time and passing through contingency tests simultaneously. In this project, we explore innovative techniques to address this challenging problem by integrating advanced polyhedral theory, approximation methods, relaxation strategies, decomposition techniques, and parallel computing. Each technique approaches the problem from a different perspective, leveraging its specific strengths to tackle distinct challenges. Each individual method has demonstrated its effectiveness in the PI’s previous research. Their integration is expected to significantly reduce the computational time required to solve the proposed complex problem. Successful completion of this project has the potential to transform the industry by enhancing optimization solvers capable of handling large-scale day-ahead energy market clearing models within strict time constraints, while incorporating AC power flow constraints. This advancement will lead to reduced overall generation costs and, consequently, increased social welfare.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

LLGOMAX: Enhancing Industry-Standard Tools for AC Optimal Unit Commitment (CRADA Final Report)

This was a collaborative effort between Lawrence Livermore National Security, LLC ("LLNS"), as manager and operator of Lawrence Livermore National Laboratory ("LLNL") and ECCO International Inc. ("Participant"). The team designed and implemented a new approach for the Security-Constrained Alternating-Current Unit Commitment (SCACUC) problem. The SCACUC problem is a mathematical optimization problem that decides which generating units should be online and how much power should be produced (and consumed) at each point of the power grid. The decisions are made so as to minimize the total cost of supplying electricity while respecting technical constraints of the power grid, both under normal and emergency conditions. The team developed a solution for SCACUC as specified in the ARPA-E Grid Optimization Competition (GOC) Challenge 3, which put forth a forward-looking version of the problem, which many features not present in today’s electricity market specifications for SCACUC.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Scalable Stochastic Transmission Expansion: A Use Case for ExaSGD

The intermittent nature of renewable energy poses new challenges for power grids due to its variable and un- certain power output. These features of renewable generation are becoming more relevant to transmission planning as grids reach higher penetration levels of renewable energy. In this paper we present an approach for transmission planning based on scalable computational approaches which enable the explicit consideration of operational uncertainties in the planning process. Using three-stage stochastic programming and the progressive hedging algorithm, we compute transmission expansion decisions on a modified RTS-GMLC test system. We augment the grid with large amounts of wind generation and consider many operational scenarios subject to wind uncertainty. This is an example of a possible use of the ExaSGD security constrained AC optimal power flow solver.

Exascale Computing Project↗
DOE OSTI2020

Probabilistic Forecasting of Generators Startups and Shutdowns in the MISO System Based on Random Forest

Solving security constrained unit commitment (SCUC) problems to plan an economical generation schedule for day-head electricity market has been an important research topic in recent years. Mixed integer programming method (MIP), the-state-of-art approach for solving SCUC problem, is known computationally hard when the number of binary status variables is large. In this paper, a machine learning-based algorithm - random forest (RF), was applied to forecast the startups (SU) and shutdowns (SD) hours of generators, based on historical hourly system condition observations in the Midcontinent Independent System Operator (MISO) system. The main purpose is to reduce the number of binary status variables, by fixing the SU/SD hours to a narrow range of high confidence. This would significantly reduce the size of the decision space, and therefore speed up SCUC solutions with reduced uncertainty.

Lin, Xinming↗
DOE OSTI2024

Solving the Grid Optimization Competition Challenge 3 Problem

The Grid Optimization Competition Challenge 3 Problem posed a multiperiod security-constrained unit commitment problem with base-case AC power flow. The problem formulation includes binary unit commitment decisions, nonlinear AC power flow and balance, dispatchable loads, and linearized contingency real power flow, among other features. This talk will present a modified consensus ADMM algorithm, which splits the problem into mixed-integer linear and nonlinear components, as a heuristic solution method for this large-scale mixed integer nonlinear program. We will present some computational results from the competition for our implementation and reflect on the challenges of participating the grid optimization competition.

AC power flow↗
DOE OSTI2016

Impacts of Short-Term Solar Power Forecasts in System Operations

Solar generation is experiencing an exponential growth in power systems worldwide and, along with wind power, is posing new challenges to power system operations. Those challenges are characterized by an increase of system variability and uncertainty across many time scales: from days, down to hours, minutes, and seconds. Much of the research in the area has focused on the effect of solar forecasting across hours or days. This paper presents a methodology to capture the effect of short-term forecasting strategies and analyzes the economic and reliability implications of utilizing a simple, yet effective forecasting method for solar PV in intra-day operations.

14 SOLAR ENERGY↗
DOE OSTI2017

An assessment of flexibility reserves in stochastic modeling at multiple timescales

Due to recent technological achievements, stochastic optimization, which inherently captures the uncertainty of intermittent resources, is being used to capture the variability and uncertainty of wind and solar resources. However, due to persistent computational limitations, it is not practical to consider all possible variable generation scenarios. As a result, a reduced number of most likely scenarios is usually considered. While this helps reduce the computational burden, it also leaves the system operator vulnerable to some risk. In order to address this issue, this paper aims at providing insight into using an explicit reserve requirement in a stochastic modeling framework in order to provide system operators with greater confidence in stochastic dispatch solutions. This is accomplished by simulating a modified version of the IEEE 118 bus system in a fully stochastic, multi-timescale framework with flexibility reserve requirements. Results show that utilizing a stochastic flexibility reserve requirement within the stochastic modeling framework offers the most reliability benefit.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Modeling Imperfect Generator Behavior in Power System Operation Models

A key component in power system operations is the use of computer models to quickly study and analyze different operating conditions and futures in an efficient manner. The output of these models are sensitive to the data used in them as well as the assumptions made during their execution. One typical assumption is that generators and load assets perfectly follow operator control signals. While this is a valid simulation assumption, generators may not always accurately follow control signals. This imperfect response of generators could impact cost and reliability metrics. This paper proposes a generator model that capture this imperfect behavior and examines its impact on production costs and reliability metrics using a steady-state power system operations model. Preliminary analysis shows that while costs remain relatively unchanged, there could be significant impacts on reliability metrics.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2015

Operational Impacts of Operating Reserve Demand Curves on Production Cost and Reliability: Preprint

The electric power industry landscape is continually evolving. As emerging technologies such as wind, solar, electric vehicles, and energy storage systems become more cost-effective and present in the system, traditional power system operating strategies will need to be reevaluated. The presence of wind and solar generation (commonly referred to as variable generation) may result in an increase in the variability and uncertainty of the net load profile. One mechanism to mitigate this is to schedule and dispatch additional operating reserves. These operating reserves aim to ensure that there is enough capacity online in the system to account for the increased variability and uncertainty occurring at finer temporal resolutions. A new operating reserve strategy, referred to as flexibility reserve, has been introduced in some regions. A similar implementation is explored in this paper, and its implications on power system operations are analyzed.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2015

Operational Impacts of Operating Reserve Demand Curves on Production Cost and Reliability

The electric power industry landscape is continually evolving. As emerging technologies such as wind, solar, electric vehicles, and energy storage systems become more cost-effective and present in the system, traditional power system operating strategies will need to be reevaluated. The presence of wind and solar generation (commonly referred to as variable generation) may result in an increase in the variability and uncertainty of the net load profile. One mechanism to mitigate this is to schedule and dispatch additional operating reserves. These operating reserves aim to ensure that there is enough capacity online in the system to account for the increased variability and uncertainty occurring at finer temporal resolutions. A new operating reserve strategy, referred to as flexibility reserve, has been introduced in some regions. A similar implementation is explored in this paper, and its implications on power system operations are analyzed.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

A Comprehensive Comparison of Current Operating Reserve Methodologies

Electric power systems are currently experiencing a paradigm shift from a traditionally static system to a system that is becoming increasingly more dynamic and variable. Emerging technologies are forcing power system operators to adapt to their performance characteristics. These technologies, such as distributed generation and energy storage systems, have changed the traditional idea of a distribution system with power flowing in one direction into a distribution system with bidirectional flows. Variable generation, in the form of wind and solar generation, also increases the variability and uncertainty in the system. As such, power system operators are revisiting the ways in which they treat this evolving power system, namely by modifying their operating reserve methodologies. This paper intends to show an in-depth analysis on different operating reserve methodologies and investigate their impacts on power system reliability and economic efficiency.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

ARPA-E Grid Optimization (GO) Competition Challenge 2

The ARPA-E Grid Optimization (GO) Competition Challenge 2, from 2020 to 2021, expanded upon the problem posed in Challenge 1 by adding adjustable transformer tap ratios, phase shifting transformers, switchable shunts, price-responsive demand, ramp rate constrained generators and loads, and fast-start unit commitment. Furthermore, Challenge 2 was a maximization problem while Challenge 1 was a minimization problem. Specifically, the economic surplus, defined as the benefit of serving load minus the cost of generation, is being maximized. It was expected that the objective value of a given solution should be positive, representing economic gain, but negative objectives from poor solutions were possible. The two code submission feature of Challenge 1 was maintained. Additionally, Divisions 3 and 4 within the competition permitted on/off switching of transmission lines (Divisions 1 and 2 did not). After the initial release of the Problem Formulation on 7/20/2020, ARPA-E Director Lane Genatowski announced Challenge 2 on 9/12/2020. The final May 31, 2021, version of the Problem Formulation was 97 pages long with 299 equations. The Challenge proceeded with 2 non-prize Events and 2 prize Events. Teams receiving Challenge 1 FOA awards and prize money were required to use the prize money to fund their Challenge 2 efforts (Georgia Institute of Technology, Global Optimal Technology, Inc., Lawrence Livermore National Laboratory, Lehigh University, Northwestern University, Artelys, Columbia, Pearl Street Technologies, Pennsylvania State University, and University of Colorado Boulder). For more information on the competition and challenge 2 see the "GO Competition Challenge 2 Information" resource below. Challenge 1 and Challenge 3 information can be found in the resources linked below.

ACOPF↗
DOE OSTI2017

Proximal Occlusion of Medium-Sized Vessels with the Penumbra Occlusion Device: A Study of Safety and Efficacy

PurposeTo retrospectively investigate the safety and efficacy of hybrid proximal coiling of various medium-sized vessels (4 to 8 mm) using the Penumbra Occlusion Device (POD).Materials and MethodsFrom October 2014 to February 2016, 37 proximal embolizations were performed with PODs in 36 patients (mean age: 50.8, range: 10–86; 29 male, 7 female). Vessel occlusions were achieved under fluoroscopic guidance using a 2.7 French microcatheter. Among the 36 vessels targeted, 16 were splenic arteries, 11 renal arteries, 4 mesenteric arteries, 3 arteriovenous fistulae, 1 iliac artery, and 1 gonadal vein. Intermittent follow-up angiography was performed to assess the flow for final occlusion. Outcomes and complications were assessed by clinical and/or imaging follow-up.ResultsTo produce proximal occlusion of the intended vessels, the POD was used alone in 19 embolizations (51.4 %). In 12 procedures (32.4 %), POD was used as a coil constrainer to secure the coil construct. In 6 procedures (16.2 %), additional embolic devices were used to achieve vessel occlusion after initial POD deployment. After a mean follow-up of 3.2 months, no POD migration was observed but two complications occurred (5.4 %): one post embolic syndrome and one extensive infarction with splenic abscess.ConclusionThe POD system allows safe and effective proximal embolization of medium-sized vessels in a variety of clinical settings.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2014

Evaluation of Production Cost Savings from Consolidation of Balancing Authorities in the US Western Interconnection under High Wind and Solar Penetration

This paper introduces a comprehensive analysis to quantify the potential savings in production cost due to consolidation of 32 US western interconnection Balancing Authorities (BAs). Three simulation scenarios are developed: current Western Electricity Coordinating Council (WECC) BAs structure, full copper-sheet consolidation, and full consolidation with transmission congestion considered. The study uses WECC Transmission Expansion Planning Policy Committee (TEPPC) model that was developed for the year 2020. The model assumes 8% wind and 3% solar energy penetration as percentage of total WECC demand in 2020. Sensitivity analyses are carried out to assess the impact of transmission hurdle rates between WECC BAs on potential benefits. The study shows savings that ranges from $400 Million (2.4% of total one year production cost) to $600 Million (3.2%) per year in thermal units production cost due to consolidation can be achieved. The copper sheet consolidation scenario shows an extra savings of $240 Million (1.4%) per year.

Balancing authorities, hurdle rates, economic disp↗