Search NASA⌕ Search

SEARCH · Search NASA

Results for “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.

At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Using hydrogen and ammonia for renewable energy storage: A geographically comprehensive techno-economic study

Hydrogen and, more recently, ammonia have received worldwide attention as energy storage media. In this work we investigate the economics of using each of these chemicals as well as the two in combination for islanded renewable energy supply systems in 15 American cities representing different climate regions throughout the country. We use an optimal combined capacity planning and scheduling model which minimizes the levelized cost of energy (LCOE) by determining optimal unit selection and size along with unit commitments, production rates, and storage inventories for each period of system operation. These periods are aggregated from full year hourly resolution data via a consecutive temporal clustering algorithm. Ammonia is generally more economical than hydrogen as a single method of energy storage. Additionally, systems which use both hydrogen and ammonia outperform those which use only one storage option and have LCOE between $\$ 0.17$/kWh and $\$ 0.28$/kWh, including full investment in renewable generation infrastructure.

25 ENERGY STORAGE↗
DOE OSTI2021

The impacts of convex piecewise linear cost formulations on AC optimal power flow

Despite strong connections through shared application areas, research efforts on power market optimization (e.g., unit commitment) and power network optimization (e.g., optimal power flow) remain largely independent. A notable illustration of this is the treatment of power generation cost functions, where nonlinear network optimization has largely used polynomial representations and market optimization has adopted piecewise linear encodings. This work combines state-of-the-art results from both lines of research to understand the best mathematical formulations of the nonlinear AC optimal power flow problem with piecewise linear generation cost functions. An extensive numerical analysis of non-convex models, linear approximations, and convex relaxations across fifty-four realistic test cases illustrates that nonlinear optimization methods are surprisingly sensitive to the mathematical formulation of piecewise linear functions. The results indicate that a poor formulation choice can slow down algorithm performance by a factor of ten, increasing the runtime from seconds to minutes. Furthermore, these results provide valuable insights into the best formulations of nonlinear optimal power flow problems with piecewise linear cost functions, an important step towards building a new generation of energy markets that incorporate the nonlinear AC power flow model.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Stochastic economic dispatch of wind power under uncertainty using clustering-based extreme scenarios

Operation of power systems with high penetrations of renewable energy sources requires tools for robust decision making under uncertainty. Stochastic economic dispatch and stochastic unit commitment are effective techniques for planning and operation under uncertainty, whose effectiveness depends on the cardinality and quality of the scenario set. Here, this article proposes a machine learning method using -means clustering for capturing relevant physical information from a large population of analog scenarios. Extreme scenario samples drawn from the clusters are used in a two-stage stochastic economic dispatch computation. The effectiveness of the proposed approach is assessed on a synthetic 200-bus system with a geographic footprint over Illinois, USA for four months from each season of WIND Toolkit data. The combination of -means clustering with importance sampling is shown to reduce the total operational cost by over 43% compared to sampling from populations based on heuristic clustering-based methods. Additionally, the variability in the mean cost is about 56% lower than the variability using Monte Carlo sampling. Moreover, the operational cost with the presented approach is shown to be close to the cost calculated based on a hindsight exact wind profile, signifying a highly accurate quantification of wind uncertainty by the presented -means clustering based sampling method.

17 WIND ENERGY↗
DOE OSTI2026

Joint scheduling of energy, fast and primary frequency response reserves in integrated transmission–distribution networks

Inverter-based distributed energy resources (DERs) connected to distribution networks (DNs) can provide fast frequency support, but their reserve deliverability depends on feeder constraints and differs from synchronous primary frequency response (PFR). Existing transmission–distribution coordination studies usually treat reserve generically or neglect feeder-level feasibility, while frequency-security scheduling studies rarely represent distribution feeders explicitly. This paper develops a bi-level day-ahead scheduling framework for integrated transmission–distribution networks that jointly clears energy, transmission-side PFR, and distribution-side fast frequency response (FFR) under exogenous hourly inertia and largest-loss inputs from an external unit commitment (UC) schedule. The transmission problem is modeled with DC-optimal power flow (OPF) and closed-form second-order cone (SOC) frequency-security constraints, whereas each DN is represented by a reserve-aware branch-flow AC-OPF so that scheduled fast reserves remain deliverable during activation. The bi-level problem is reformulated through Karush–Kuhn–Tucker (KKT) conditions into a mixed-integer SOC program, and a penalty term is used to tighten the distribution-network relaxation. In the reduced test system, lower exogenous inertia increased the required primary response from 179.64 MW to 191.08 MW, distribution-side fast response reduced total frequency-response procurement by up to 4.9%, and neglecting distribution constraints overstated the combined distribution-side energy and reserve award by up to 18%. In the expanded study, the largest case was solved in 2.02 s with a 0.00% optimality gap. Time-domain simulations kept the frequency nadir above 59.0 Hz in all tested hours. These results demonstrate the value of fast-response modeling and distribution-feasible reserve delivery in coordinated market clearing.

Noh, Seung-Gil↗
DOE OSTI2018

Use-Phase Drives Lithium-Ion Battery Life Cycle Environmental Impacts When Used for Frequency Regulation

Battery storage systems are attractive alternatives to conventional generators for frequency regulation due to their fast response time, high cycle efficiency, flexible scale, and decreasing cost. However, their implementation does not consistently reduce environmental impacts. To assess these impacts, we employed a life cycle assessment (LCA) framework. Our framework couples cradle-to-gate and end-of-life LCA data on lithium-ion batteries with a unit commitment and dispatch model. The model is run on a 9-bus power system with energy storage used for frequency regulation. The addition of energy storage changes generator commitment and dispatch, causing changes in the quantities of each fuel type consumed. This results in increased environmental impacts in most scenarios. The impacts caused by the changes in the power system operation (i.e., use-phase impacts) outweigh upstream and end-of-life impacts in the majority of scenarios analyzed with the magnitude most influenced by electricity mix and fuel price. Of parameters specific to the battery, round trip efficiency has the greatest effect.

25 ENERGY STORAGE↗
DOE OSTI2019

Approximate Mixed-Integer Programming Solution with Machine Learning Technique and Linear Programming Relaxation

Security constrained unit commitment (SCUC) is solved in the day-ahead electricity market to determine electricity generation schedule for the following date. The associated mixed integer programming (MIP) problem is known computationally hard. Heuristics like rounding can be computationally efficient for solving large MIP problems however often produces lower quality solutions due to discrepancy between MIP solution and linear programming relaxation (LPR) solution, i.e., variables taking different discrete values between MIP and LPR solutions. In this work, we applied the machine learning-based algorithms—Classification and Regression Tree (CART) model and random forest (RF) model—to obtain an LPR-based approximation that close to an MIP solution without actually solving the MIP problem. By computing feature importance derived from the CART and RF models, we assessed the link between coefficients in the MIP model, the LPR solution, and the optimal solution of the MIP problem. We applied the CART and RF approaches to solve SCUC problems using day-ahead energy market cases from Midcontinent Independent System Operator. The results show that our method is computationally efficient and could achieve an approximation sufficiently close to the SCUC solution with <0.15% error and ~78% reduction in the discrepancies between LPR and MIP solutions.

Lin, Xinming↗
DOE OSTI2023

Grid Optimization Competition on Synthetic and Industrial Power Systems

This paper summarizes a grid optimization (GO) competition effort in the United States to find the best solution strategies for up to interconnect-scale power system networks with around 32,000 buses. The optimization problem is a mixedinteger, non-convex non-linear problem, (MINLP) and includes discrete variables such as unit commitment and line switching, control settings (transformer taps and phase shifters with impedance correction tables), and bus shunts. The case study includes six actual industry grids as well as 16 realistic synthetic grids created by three different dataset teams. The winners are selected and ranked based on scoring criteria, which consider the solution quality (such as objective functions) within time limits. Nine winner teams are selected from 26 competitor teams. The results achieved by different teams are described and the performance of different algorithms on synthetic grids and actual industry grids are compared and analyzed.

mixed-integer non-linear programming↗
DOE OSTI2022

System Resilience Benefits of Dual-Fuel Capable Generators

The growing dependency on natural gas (NG) for power generation raises challenges for ensuring the resilience of power systems during extreme cold weather. Dual-fuel capable generators that can switch from burning NG to distillate fuel oil during a NG shortage offer one way to alleviate these challenges. In this study, the impacts of gas unavailability on the IEEE 73- bus reliability test system (RTS) with 2019 updated generation mixture are explored. An extension to the standard production cost model formulation of unit commitment and economic dis- patch is proposed to allow representation of dual-fuel capable generators that can switch fuels between NG and distillate oil with a specified oil tank capacity and tank refueling capability. The operation of the system under gas unavailability with 0%, 25%, 50%, 75%, and 100% of combined cycle and combustion turbine generators as dual-fuel capable with a one-day supply of fuel oil are simulated using PLEXOS, a production cost model. The dual-fuel generator performance, unserved energy, and system costs are fully assessed.

duel-fuel generator performance↗
DOE OSTI2024

A Model for Hybrid Systems for Production Cost Modeling Studies Considering Ancillary Services

This paper introduces a model for simulating hybrid plants participating in energy and ancillary services for bulk power system studies. The model considers a hybrid plant comprised of a renewable energy source, a thermal power unit, a storage unit, fixed power loads, or any combination of these technologies. The model focuses on Production Cost Modeling (PCM) studies under the assumption of centralized dispatch. We present an example case study to illustrate the use of the model in a single-stage production cost model similar to those conducted by planning agencies. We explore the allocation of behind-the-meter ancillary services products and total energy participation to hybrid plant sub-assets and the resulting impacts on the system's ancillary service allocation. The model is implemented and simulated in a unit commitment problem in the RTS test system, which was modified to include a hybrid plant asset.

ancillary services↗
DOE OSTI2024

Improved Evaluation of Large Network Matrices for Linear Power Flow Within Optimization Problems

This work presents methods for evaluating the Power Transfer Distribution Factor (PTDF) and Line Outage Distribution Factor (LODF) matrices by employing sparse linear algebra for large-scale computing applications. These matrices play a critical role in many power system applications, such as the Unit Commitment Problem (UC), pre- and post-contingency power flow analysis, and transmission expansion. These matrices are typically dense, which means they require a significant amount of time and memory to be computed for large networks. However, by analyzing the structure of the matrices and their computation method, it is possible to use reduced memory methods based on sparse matrix operations. This paper shows that sparse linear algebra algorithms are faster and require less memory and time than traditional dense approaches. Additionally, we explore the effect of matrix sparsification by eliminating trailing digits on power flow calculations.

large scale↗
DOE OSTI2018

Combined Transmission and Distribution Test System to Study High Penetration of Distributed Solar Generation

Transmission and distribution (T&D) standard test systems are currently defined separately, and are critical for researchers and engineers to test new solutions. Growing installation of energy resources in distribution and sub-transmission systems is creating higher interactions between T&D systems. This paper proposes a standard test system to study T&D interactions for high penetration of solar photovoltaic (PV) generation. The proposed test system can be used to study and propose solutions to problems such as reverse power flows and voltage control affected by PV variability. The proposed test system combines the transmission IEEE 118 bus system, with the distribution IEEE 123-node feeder. The original test systems are significantly enhanced by adding high penetration of solar PV generation with corresponding full-year minute-resolution chronological data. Conventional generation chronology is achieved by adding dispatch and unit commitment data for AC optimal power flow solutions. And chronological load data is added in both aggregated and disaggregated fashions. Sample applications of the data are provided highlighting voltage control challenges, and the proposed system is made available openly online.

Samaan, Nader A.↗
DOE OSTI2019

Multi-operator Production Cost Modeling

As contiguous power systems are often operated by several regional system operators, optimization models have traditionally been designed to generate optimal system-wide scheduling decisions. This report introduces an approach to reflect the existence of multiple system operators making scheduling decisions within an interconnected power system. The approach is explicitly designed to reflect each operator's limited knowledge about other regions when making scheduling decisions. Detailed analysis using a modified IEEE Reliability Test System reveals that the approach mimics real system operations by providing imperfect information to each operator about external operations and external constraints. The approach scales to a 78,000-bus model of the Eastern Interconnection, where a regression analysis provides quantitative insight into which constraints cause inefficiencies when improperly managed in a multi-operator simulation. Because the approach generates distinct regional scheduling problems, unit commitment and economic dispatch simulations can be applied to individual regions and solved in parallel, enabling production cost simulations at a previously intractable scale.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

An Asynchronous Bundle-Trust-Region Method for Dual Decomposition of Stochastic Mixed-Integer Programming

Here, we present an asynchronous bundle-trust-region algorithm within the context of Lagrangian dual decomposition for stochastic mixed-integer programs. The approach solves the Lagrangian master problem by using a bundle method with a trust-region constraint. This scheme enables asynchronous computations and can thus help mitigate severe load imbalance issues (associated with the solution of scenario subproblems) and improve parallel efficiency. We provide a convergence analysis and an implementation of the proposed scheme. We also present extensive numerical results on eighty instances of a large-scale stochastic unit commitment problem, and demonstrate that the proposed approach provides significant reductions in solution time and achieves strong scaling.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

PowerSystems.jl and PowerSimulations.jl [SWR-18-23]

Data structures in Julia to enable power systems analysis. Also, PowerSimulations.jl is a Julia package for power system modeling and simulation found at https://github.com/NREL/PowerSimulations.jl. The objectives of the package are: Provide a flexible modeling framework that can accommodate problems of different complexity and at different time-scales. Streamline the construction of large scale optimization problems to avoid repetition of work when adding/modifying model details. Exploit Julia's capabilities to improve computational performance of large scale power system simulations. The flexible modeling framework is enabled through a modular set of capabilities that enable scalable power system analysis and exploration of new analysis methods. The modularity of PowerSimulations results from the structure of the simulations enabled by the package: Simulations define a set of problems that can be solved using numerical techniques. Problems are generated by expressing model formulations against system data For example, an annual production cost modeling simulation can be created by formulating a unit commitment model against system data to assemble a set of 365 daily time-coupled scheduling problems.

Lara, Jose Daniel↗
DOE OSTI2019

Egret v. 0.1 (beta)

Egret is a package that provides tools for building electric grid optimization models. This software includes objects and functions for parsing, storing, and managing electric grid data. Egret also includes the following electric grid models: - ACOPF (alternating current optimal power flow) - DCOPF – a linearized transmission model - ACOPF relaxations - Unit commitment - Economic dispatch

Knueven, Bernard↗
DOE OSTI2019

FESTIV (Flexible Energy Scheduling Tool for Integrating Variable generation) [SWR-14-11]

Flexible Energy Scheduling Tool for Integrating Variable generation (FESTIV) is a tool that simulates the realistic representation of steady-state power system operations and scheduling performed by an independent system operator, regional transmission organization, or a vertically integrated regulated utility. The tool integrates security-constrained unit commitment (SCUC), security-constrained economic dispatch (SCED), and automatic generation control (AGC) into one model. These are the three modes of power system scheduling that are used to meet the changing net demand. Each of these submodels have different time characteristics and are used to meet the changing demand at different time resolutions and horizons. This allows multiple timescales to be studied where FESTIV accounts for intertemporal coupling between each submodel and the submodels themselves. It does this while having different objectives between the submodels; commitment of resources, dispatch and reserve commitment, and finally control.

Ela, Erik↗