Search NASA⌕ Search

SEARCH · Search NASA

Results for “stochastic unit commitment”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

31 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Assessment of wind power scenario creation methods for stochastic power systems operations

Probabilistic scenarios of renewable energy production, such as wind, have been gaining popularity for use in stochastic variants of power systems operations scheduling problems, allowing for optimal decision-making under uncertainty. The quality of the scenarios has a direct impact on the value of the resulting decisions, but until now, methods for creating scenarios have not been compared under realistic operational conditions. Here, we compare the quality of scenario sets created using three different methods, based on a simulated re-enactment of stochastic day-ahead unit commitment and subsequent dispatch for a realistic test system. We create scenarios using a dataset of forecasted and actual wind power values, scaled to evaluate the effects of increasing wind penetration levels. We show that the choice of scenario set can significantly impact system operating cost, renewable energy use, and the ability of the system to meet demand. This result has implications for the ability of system operators to efficiently integrate renewable production into their day-ahead planning, highlighting the need for the use of performance-based assessments for scenario evaluation.

17 WIND ENERGY↗
DOE OSTI2020

mpi-sppy: Optimization Under Uncertainty for Pyomo

We introduce a new Pyomo extension for optimization under uncertainty, mpi-sppy. This extension allows for the mixing of, and sharing of information between, various algorithmic approaches for optimization under uncertainty. We will discuss the underlying architecture and assess the performance and scalability of mpi-sppy on various systems, including HPC clusters.

27 ARPA - Advanced Research Projects Agency-Energy↗
DOE OSTI2024

Data-Driven Unit Commitment Refinement - a Scalable Approach for Complex Modern Power Grids

Integration of renewable generation, which is often intermittent and decentralized, substantially increases the stochasticity and complexity of power grid operations. Future power systems planning will require significant computational capability to evaluate balance between demand and supply under varying conditions, both temporally and spatially. The standard approach for generation unit commitment is to use mixed-integer linear programming to find the optimal generation schedule considering ramping and generator constraints. In the future grid this poses computational scalability challenges because generation and demand are not known with certainty due to stochasticity in weather and complexity of the grid. To address this challenge, we present a data-driven unit commitment approach that can efficiently include stochastic weather impacts and contingency considerations to improve unit commitment. Our approach uses graph-based data analytics techniques on solutions to the security constrained (and possibly stochastic) economic dispatch problem to identify potential improvements to a given unit commitment. Recent breakthroughs in fully-parallel stochastic economic dispatch software allow this approach to be scalably deployed. Simulations on synthetic South Carolina and Texas grids show this method can improve grid reliability with security constraints over a set of contingencies, while also meaningfully lowering total generation cost.

Holt, Timothy↗
DOE OSTI2020

Uncertainty Quantification for Capacity Expansion Planning

This report quantifies the uncertainty in output decisions from a Capacity Expansion Planning (CEP) model. The need to understand how uncertainties within CEP models and modeling assumptions affect Quantities of Interest (QoIs) such as expansion and operating costs, as well as expansion decisions remains an ongoing challenge in scientific research and industrial operations. This area of research is particularly important for models which seek to capture how large networks will evolve and operate under increased sources of variable generation, i.e., higher penetration of renewable technologies such as solar and wind generators. Uncertainty quantification (UQ) of CEP models which estimate expansion costs and decisions, and production cost models which estimate operating costs and dispatch decisions, is a key focus of research at NREL. The Regional Energy Deployment System (ReEDS) represents a state-of-the-art CEP model and considers a range of possible grid evolutions in an attempt to identify key drivers, ramifications, and decisions which contribute to better informed investment and policy decisions. However, research to quantify how uncertainties and model assumptions, such as unit commitment (UC), within ReEDS may be affecting its outputs remains challenging due to to size and complexity of the model

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Stochastic Models, Indices & Optimization Algorithms for Pricing & Hedging Reliability Risks in Modern Power Grids: Data Plan - Princeton

We collected and cleaned the synthetic grid data produced by NREL for the Texas and New York synthetic grids. We developed a high dimensional joint stochastic model for load at the zone level, and solar and wind power productions at the asset level, capturing the spatial and temporal dependencies between all the variables, and demonstrated how such a model could be fitted to historical data. We designed and implemented a simulation engine which can produce Monte Carlo scenarios for the hourly day-ahead values of load, and solar and wind power productions at the spatial and temporal resolutions of the historical data used to fit the model. Finally we developed an open-source Python package which can, from an input grid model, efficiently use forecasts and large numbers of Monte Carlo scenarios to provide unit commitment and economic dispatch for each of these scenarios. The high dimensional stochastic model and the subsequent Monte Carlo simulation engine were implemented in the package PGscen and the corresponding UC and ED optimization programs in the package Vatic.

14 SOLAR ENERGY↗
DOE OSTI2021

Stochastic Continuous-time Flexibility Scheduling and Pricing in Wholesale Electricity Markets

Large-scale integration of intermittent renewable energy sources (RES) is calling for additional flexibility resources as well as more advanced modeling and optimization techniques to account for the increasing uncertainty and variability in power systems operation. As the RES integration gains momentum, the magnitude and frequency of their variations increase, which may trigger ramping scarcity events in real-time power systems operation. This necessitates revisiting the present definition of power systems flexibility and reserve services to reflect their robustness and adequacy towards sub-interval variations of the load and RES, as well as adjusting the operation models to accommodate the new reserve services. This project took a fundamental approach and aimed at developing continuous-time scheduling and pricing model that accurately models the continuous-time variations of load and RES and efficiently deploys the ramping capability of flexible resources to compensate the sources of variability and uncertainty in the market. In this regard, this project pursued the following goals: Developing stochastic multi-fidelity continuous-time optimization models for scheduling of energy storage (ES) systems and flexible loads in wholesale energy markets; Developing the theory and practices of continuous-time locational marginal pricing for valuating energy storage systems and flexible loads in wholesale energy markets; Developing function space solution approach to convert the proposed stochastic multi-fidelity continuous-time optimization models into tractable mixed-integer linear optimization models; and Defining flexibility reserve as a new type of reserve in markets that would enable ultimate participation of energy storage devices in provision of services to compensate the variability and uncertainty of RES in electricity markets. This project successfully completed all five major tasks defined in the SOPO, and produced 8 high-impact journal papers, 6 conference papers, 3 published U.S. patents, and one web-based software for continuous-time operation optimization of power systems. The application of the proposed flexibility reserve and the stochastic multi-fidelity continuous-time operation scheduling models would modify the forward commitment and schedule of generating units, ES devices and flexible loads, and would line up the resources in such a way that the composition of available resources is better prepared to respond to the sub-hourly variations of the load and renewable resources in real-time operation. Therefore, this project paves the way to sustainable, reliable, and economic integration of renewable energy resources in power system, supporting the progress towards reaching the national targets on energy independence. Even if the proposed models offers a radically different point of view as compared to existing models, it does not alter fundamentally the architecture of power systems operations, nor the complexity of the scheduling problem, so the integration of this project in power systems is extremely practical.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Power System Resilience Enhancement in Typhoons Using a Three-Stage Day-Ahead Unit Commitment

In this work, we propose a three-stage resilient unit commitment model which considers uncertain typhoon paths and line outages to improve the power system resilience against typhoon events. The proposed solution coordinates resources in response to the worst-case scenario for each possible typhoon path. The optimal decision is based on the characterization of the power system schedule into three stages of preventive control, emergency control, and restoration. Preventive control is performed before the typhoon occurs by quickly adjusting the three-stage resilient unit commitment schedule; emergency control is conducted during the typhoon by shedding local loads to meet the power balance, while other control strategies are assumed to be unavailable due to possible interruptions in the communication system; restoration is realized after the typhoon, when resources are optimally dispatched to repair the outages of critical devices and recover the normal operation state of the power system quickly. Considering the typhoon path uncertainty, we have introduced a stochastic model for possible typhoon paths where all possible affected lines along each typhoon path are assumed to be on outage during the typhoon. Accordingly, we explore the strategy for co-optimizing the three stages in unit commitment. The proposed model is tested on the IEEE 118-bus system and the real-world provincial system to verify its effectiveness.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Sparow-Examples

SAND2026-16701O SPAROW-Examples software provides a repository of stochastic programming examples designed to demonstrate Sandia's SPAROW optimization library. This resource helps users learn to develop complex applications with SPAROW by offering reference implementations that can be used to test and enhance new optimization solvers. The library offers a diverse range of simple and complex exemplars, including those related to power grid applications such as unit commitment and expansion planning. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Hart, William [Sandia National Lab. (SNL-CA), Live↗
DOE OSTI2024

A Data-Driven Methodology for Contextual Unit Commitment Using Regression Residuals

Day after day, system operators are faced with the challenge of taking unit commitment (UC) decisions under uncertain net load conditions. The standard operating procedure for taking UC decisions begins by leveraging auxiliary data on covariates (such as the day of the week or latest weather information) to generate a point prediction for net load, which is used in solving a deterministic UC problem. Such an approach, however, is known to deliver a notoriously poor out-of-sample (OOS) performance, as it completely disregards the stochastic nature of net load. While stochastic programming models explicitly represent uncertainty, they mostly do so using a generic set of scenarios that neglect covariate observations, squandering useful auxiliary data that could be harnessed to glean insights into uncertainty. In this article, we discuss a contextual stochastic optimization approach to UC, which effectively exploits covariate observations while explicitly assessing uncertainty so as to boost the OOS performance of UC decisions. The key thrust of our approach is to leverage regression models, along with their empirical residuals, to set up and solve sample average approximation problems. Not only do we prove that our approach satisfies the requisite conditions for asymptotic optimality and consistency laid out in (Kannan et al., 2022), but we also assess its performance on several case studies conducted using real-world data collected in California ISO and New York ISO grids. In conclusion, results show that the proposed approach can significantly improve OOS performance compared to alternative methods proposed in the literature under varying dataset sizes.

Yurdakul, Ogun↗
DOE OSTI2026

Near-Optimal Performance of Stochastic Model Predictive Control

Here, this article presents a regret analysis for stochastic model predictive control (SMPC) in linear systems with quadratic performance index and additive and multiplicative uncertainties. Under a finite support assumption, the problem can be cast as a finite-dimensional quadratic program, but the problem becomes quickly intractable as the problem size grows exponentially in the horizon length. SMPC aims to compute approximate solutions by solving a sequence of problems with truncated prediction horizons and committing the solution in a receding-horizon fashion. Although this approach is widely used in practice, its performance relative to the optimal solution is not well understood. This article reports for the first time a rigorous near-optimal performance guarantee of SMPC: under stabilizability and detectability conditions, the regret of SMPC is exponentially small in the prediction horizon length, allowing SMPC to achieve near-optimal performance at a substantially reduced computational expense.

93E20, 93B45↗
DOE OSTI2025

Flexible Resource Scheduler for FAST-DERMS (FRS-FASTDERMS) v0.9

The Flexible Resource Scheduler is a hierarchical controller that manages the distributed energy resources in a distribution substation or distribution feeder to provide a firm commitment of power flow at the substation or feeder head to be scheduled in transmission-level markets as an aggregated demand resource. It is the reference controller for the FAST-DERMS Architecture, developed in tandem with the architecture under the DOE FAST-DERMS project. It is comprised of a day-ahead stochastic optimization, which schedules substation power flow and reserves, a intra-hour MPC, which generates dispatch base points for DER, and a real-time PID controller maintaining that dispatches DER to maintain the substation power around the base points. The repository also includes a representative aggregator controller, and all of the necessary components to run a simulation using PNNL's GridAPPS-D software with the controller.

MacDonald, Jason [Lawrence Berkeley National Labor↗
DOE OSTI2026

Statistical Uncertainty of Inhalation Dose Coefficients in Consequence Management: Propagated Dose Uncertainty in ICRP 66 Human Respiratory Tract Model

Reference inhalation dose models rely on deterministic biokinetics and reference computational phantoms, limiting their applicability to the variability present in population-specific exposures encountered in emergency response scenarios. Here, this study introduces REDCAL, a Python-based computational framework developed to propagate uncertainty in inhalation dose coefficients using the International Commission on Radiological Protection (ICRP) Publication 66 Human Respiratory Tract Model. REDCAL integrates ICRP deposition and clearance models, systemic biokinetics, and governing physics principles, and leverages Sandia National Laboratories’ Dakota toolkit for uncertainty quantification via Latin Hypercube Sampling. REDCAL was validated against DCAL, with biokinetic retention results differing by less than 1% and effective dose coefficients by less than 2% across all tested radionuclides. Stochastic sampling introduced variability in dose coefficients, with geometric standard deviations (GSD) in committed effective dose coefficients (CEDC) ranging from 1.0 to 1.5, based on lognormal distribution fits. Analysis demonstrated that variations in the activity median aerodynamic diameter (AMAD) notably influenced the computed CEDC values. Smaller particles (<1 µm) increased doses by 20–30% due to deeper lung deposition and prolonged retention for alpha emitting radionuclides, such as 241 Am and 239 Pu. Radionuclides with fast clearance, such as 133 I, demonstrated a dose reduction exceeding 50%, as AMAD increased beyond 5 µm due to upper airway deposition and rapid mucociliary clearance. The greatest GSD among the radionuclides reported in this study was for 241 Am. In most cases, the largest GSDs in the CEDC were associated with larger particle sizes, an expected outcome, as ICRP Publication 66 defines GSD in particle size as a function of AMAD, resulting in an extended tail of the lognormal distribution. The findings support improved inhalation dose assessments and enhance consequence management strategies for the U.S. Federal Radiological Monitoring and Assessment Center by quantifying uncertainty in dose coefficients and strengthening decision-making for emergency response scenarios.

Biokinetic Modeling↗
DOE OSTI2020

Probabilistic Zonal Reserve Requirements for Improved Energy Deliverability with Wind Power

In power systems with high penetration of renewable energy resources, uncertainty and variability of these stochastic resources introduce additional challenges for the operation of power systems. To improve the power system's reliability in the face of uncertainty, reserves are required as additional generation capacity to rebalance the power system following random disturbances. However, reserve deliverability is not guaranteed, because it may encounter potential transmission line congestion. Zonal reserve requirements can address this issue, but operators lack efficient ways to allocate reserves to zones while accounting for wind power forecast uncertainty. Herein we propose a methodology for probabilistic zonal reserve requirements to address wind power forecast uncertainties. This method estimates the probability distribution of line flows based on the system generation margin and injection shift factor. This estimate is then used to construct pre-defined and post-zonal reserve requirements. Case studies demonstrate that the proposed method efficiently schedules energy and reserves to balance energy and manage deliverability with wind power forecast uncertainty.We also discuss operational implications of the proposed method.

17 WIND ENERGY↗