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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.

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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
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↗
DOE OSTI2023

G-PST Inertia Monitoring Tool [SWR-23-54]

This tool provides interfaces for ingesting real-time unit commitment data (e.g., from a grid operator's control room energy management system) and calculating system inertia estimates based on the H-constant method, before relaying those estimates to customizable visualization or logging outputs.

Stephen, Gordon↗
DOE OSTI2021

HIPPO Software

HIPPO software is a Python implementation of the HIPPO technology. The HIPPO technology provides the computational and modeling capabilities for managing electricity market operations and for designing and prototyping future markets. The current software can solve the day-ahead unit commitment problem and perform simultaneous feasibility test. It can be used to handle industry scale electricity market.

Peng, Fan↗
DOE OSTI2020

Optimization-Driven Scenario Grouping

Scenario decomposition algorithms for stochastic programs compute bounds by dualizing all nonanticipativity constraints and solving individual scenario problems independently. Here, we develop an approach that improves on these bounds by reinforcing a carefully chosen subset of nonanticipativity constraints, effectively placing scenarios into groups. Specifically, we formulate an optimization problem for grouping scenarios that aims to improve the bound by optimizing a proxy metric based on information obtained from evaluating a subset of candidate feasible solutions. We show that the proposed grouping problem is NP-hard in general, identify a polynomially solvable case, and present two formulations for solving the problem: a matching formulation for a special case and a mixed-integer programming formulation for the general case. We use the proposed grouping scheme as a preprocessing step for a particular scenario decomposition algorithm and demonstrate its effectiveness in solving standard test instances of two-stage 0–1 stochastic programs. Using this approach, we are able to prove optimality for all previously unsolved instances of a standard test set. Additionally, we implement this scheme as a preprocessing step for PySP, a publicly available and widely used implementation of progressive hedging, and compare this grouping approach with standard grouping approaches on large-scale stochastic unit commitment instances. Finally, the idea is extended to propose a finitely convergent algorithm for two-stage stochastic programs with a finite feasible region.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Decomposable Formulation of Transmission Constraints for Decentralized Power Systems Optimization

One of the most complicating factors in decentralized solution methods for a broad range of power system optimization problems is the modeling of power flow equations. Existing formulations for direct current power flows either have limited scalability or are very dense and unstructured, making them unsuitable for large-scale decentralized studies. Here, in this work, we present a novel sparsified variant of the injection shift factors formulation, which has a decomposable block-diagonal structure and scales well for large systems. We also propose a decentralized solution method, based on the alternating direction multiplier method, that efficiently handles transmission line outages in N-1 security requirements. Benchmarks on multizonal security-constrained unit commitment problems show that the proposed formulation and algorithm can reliably and efficiently solve interconnection-level test systems with up to 6,515 buses with no convergence or numerical issues.

Alternating-method of multipliers (ADMM)↗
DOE OSTI2020

Practice Summary: Improved Reliability via Optimization in Residential Microgrids

Southern Company is an electric utility that is embracing residential microgrids as a platform for distributed energy. The first residential microgrid in the southeastern United States was constructed in Hoover, Alabama, as a testbed for development. To operate the microgrid, we developed an optimization model for unit commitment and economic dispatch. We report the optimization model was implemented in a microgrid central controller and tested under various operational scenarios. Challenges also include developing a pricing algorithm to deliver price forecasts to the residential homes in the microgrid and open-source software restrictions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

Impacts of High Variable Renewable Energy Futures on Wholesale Electricity Prices, and on Electric-Sector Decision Making

Increasing penetrations of variable renewable energy (VRE) can affect wholesale electricity price patterns and make them meaningfully different from past, traditional price patterns. Many long-lasting decisions for supply- and demand-side electricity infrastructure and programs are based on historical observations or assume a business-as-usual future with low shares of VRE. Our motivating question is whether certain electric-sector decisions that are made based on assumptions reflecting low VRE levels will still achieve their intended objective in a high VRE future. We qualitatively describe how various decisions may change with higher shares of VRE and outline an analytical framework for quantitatively evaluating the impacts of VRE on long-lasting decisions. We then present results from detailed electricity market simulations with capacity expansion and unit commitment models for multiple regions of the U.S. for low and high VRE futures. We find a general decrease in average annual hourly wholesale energy prices with more VRE penetration, increased price volatility and frequency of very low-priced hours, and changing diurnal price patterns. Ancillary service prices rise substantially and peak net-load hours with high capacity value are shifted increasingly into the evening, particularly for high solar futures. While in this report we only highlight qualitatively the possible impact of these altered price patterns on other demand- and supply-side electric sector decisions, the core set of electricity market prices derived here provides a foundation for later planned quantitative evaluations of these decisions in low and high VRE futures.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

Operational Analysis of the Eastern Interconnection at Very High Renewable Penetrations

The primary objective of the study reported here is to build on work by Mai et al. (2012) and Brinkman (2015) by performing detailed modeling of the U.S. Eastern Interconnection operations with approximately 70% of all energy coming from wind and solar generation. The study assesses the ability to balance supply and demand for electricity using a unit commitment and economic dispatch model with a five-minute time resolution. In addition, the study explores the implications of different amounts of physical and institutional flexibility for balancing. We also present an analysis of system “stress” by quantifying various metrics, such as the amount of spare online capacity, penetration levels of non-synchronous generators, day-ahead compared to real-time curtailment, and start-ups of peaking generation.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

Cross-Border Electricity Trade between India and Sri Lanka: Impact on Power System Operations

Recent developments in South Asia have the potential to increase the incentives for cross-border energy trade (CBET) between India and Sri Lanka. India's surplus power, including from its rapid expansion of wind and solar generation, provides an incentive for it to increase investment returns through exports. Sri Lanka is considering options to increase its power supply, and CBET could alleviate the need for investments in new thermal generation capacity, while giving it access to India's large and diverse power system. Finally, CBET can facilitate renewable energy (RE) integration by allowing the two countries to more efficiently harness balancing resources and smooth out wind and solar variability over larger geographic areas. This study builds on several prior analyses of a potential high-voltage direct current transmission link between India and Sri Lanka. Uniquely, the study makes use of a detailed one-year, 30-minute resolution unit commitment and economic dispatch (UC&ED) model of both countries to examine the technical and economic impacts of CBET between India and Sri Lanka in the year 2025. The study finds that CBET, enabled by a 500-MW high voltage direct current transmission link, could generate annual production cost savings of USD 180 million and improve power system operations. Through better utilization of the most efficient generators, generation from relatively expensive natural gas plants in Sri Lanka is displaced by lower cost imports from India. In addition, increased flexibility through exports reduces RE curtailment in India's Southern region by 400 GWh annually. While the study captures many previously unconsidered intertemporal operational constraints, it does not provide a complete characterization of system operations with a high-voltage direct current interconnection. Additional work should include a dynamic analysis of system stability following interruptions to the direct current interconnection, an AC power flow analysis and consideration of any non-technical barriers to trade which might prevent the optimization of CBET between India and Sri Lanka at 30-minute timescales.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

Nuclear Technology R&D Strategies in an Era of Energy Price Uncertainty

This project assessed technology strategies for nuclear power plants that could improve economic competitiveness in uncertain future electricity markets. Heat storage is a promising class of technologies that would allow reactors to produce steam at maximum power while giving the plant operator the flexibility to sell more electricity at higher prices. We used the Texas (ERCOT) and New England (ISO-NE) electricity markets to evaluate future market scenarios for heat storage. Hourly unit commitment and capacity change models were used to model short- and long-term market dynamics. Three heat storage technologies were identified as the most promising for nearterm deployment with existing light-water reactors: steam accumulators, twotank molten salt storage, and high-temperature-compatible concrete. We developed technoeconomic models for each of these technologies to estimate direct, overnight capital costs based on the output power [MW], energy storage capacity [MWh or GJ th ], and heat loss rate [%/hr]. Our modeling results found that adding heat storage to a nuclear power plant could increase net revenue in some cases. The greatest improvements were seen when increasing the heat storage power output with a separate steam turbine. Increasing energy storage capacity was beneficial through 12 hours of full-output storage time, but there was little to no benefit for higher energy/ power ratios. High natural gas prices as well as increasing amounts of solar PV and wind were both beneficial to storage economics, while high demand and load growth and a carbon tax were helpful in some scenarios. We recommend that heat storage systems for nuclear power plants be developed with multiple future markets in mind, namely electricity generation, operating reserves, long-term capacity, and industrial heat. Heat storage system Integration risk should be mitigated early in the development process to accelerate deployments.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2019

Cross-Border Energy Trade between Nepal and India: Assessment of Trading Opportunities

This study builds on several prior analyses of cross-border energy trading in South Asia to explore the potential trading opportunities between India, Nepal, and the larger South Asia region, including Bangladesh and Bhutan. It uses a detailed one-year, 1-hour resolution unit commitment and economic dispatch model of the South Asia power system to examine the technical and economic impacts of imports and exports under different assumptions about hydropower development in Nepal and regional trade agreements in the year 2022.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Electric Power Grid and Natural Gas Network Operations and Coordination

In this report, we present findings from three studies related to the coordination of natural gas and electricity system operations. We first propose and demonstrate a modeling platform for examining the interdependence of natural gas and electricity networks based on a direct current unit-commitment and economic dispatch model for the power system and a transient hydraulic gas model for the gas system. We use this platform to analyze the value of day-ahead coordination of power and natural gas network operations and to show the importance of considering gas system constraints when analyzing power systems operation with high penetration of gas generators and variable renewable energy sources. In the second study, we utilize our modeling platform to consider the U.S. Federal Energy Regulatory Commission (FERC) Order 809, issued in 2015 to improve day-ahead and intraday coordination of power and gas systems. Finally, in the third study we expand our modeling platform to focus on market-based coordination of electricity and natural gas system operations for a real system, namely a subset of the power and gas networks in the Front Range region of Colorado. We use real system data to evaluate the benefits of coordination operations under different conditions, including different levels of renewable penetration and the use of time-variant, shaped flow nominations. Our results indicate that coordination at various timescales can contribute to a reduction in curtailed gas in high-stress periods (such as those with large ramps in gas offtakes) and a reduction in energy consumption of gas compressor stations. We find that intraday coordination can reduce total power system production costs and natural gas deliverability constraints, yielding cost and reliability benefits. We observe these benefits for the test system as well as in the Colorado case study, where we find that coordination and shaped flows may provide additional value for systems with high penetration of variable renewable energy. Together, these three studies demonstrate a pathway for integrated gas and electricity grid modeling and for studying the benefits of coordinated operations of these increasingly interdependent energy systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

Mixed Integer Nonlinear Programming Approaches to Enhance Resiliency and Response Strategies in Critical Infrastructure

Motivation. Critical infrastructures are large, complex engineered systems that must be operated robustly under abnormal conditions resulting from natural hazards or intentional acts. For example, electric power systems must be robust to line faults, water utilities must rapidly mitigate contamination incidents, and computing networks must adapt to adversarial intrusions to protect critical information. Problem. It is difficult for decision-makers within resiliency analysis in critical infrastructure to optimize designs and develop effective response strategies that can account for uncertainties. Facing incomplete information and the sheer scope that a natural hazard or attack vector may incorporate, response can be ineffective without reliable, scalable decision support tools. These problems are intrinsically nonlinear and involve discrete decisions, and unfortunately, existing off-the-shelf mathematical programming methods cannot support optimization-based decision-making of these nonlinear at scale. Method/Approach and Results. This project emphasized development of fundamental optimization strategies that supported real- time mitigation and response for critical infrastructures. In particular, the project developed multi- tree approaches based on piecewise outer-approximations for solution of mixed-integer nonlinear programming (MINLP) problems. These techniques alternate between an MILP or MISOCP relaxation to obtain a lower bound and candidate discrete solutions and an NLP subproblem to obtain upper bounds. Using tailored relaxations based on problem structure, these methods were used to solve several key applications in resilience and response of critical infrastructure. This work resulted in two open-source, copyrighted software packages: CORAMIN (https://github.com/Coramin/Coramin) -- an object-oriented mathematical programming framework that supports tailored multi-tree algorithms for solution of large- scale mixed-integer nonlinear programming; and EGRET (Electrical Grid Research and Engineering Toolkit) (https://github.com/grid- parity-exchange/Egret) -- a declarative mathematical programming framework built upon CORAMIN and Pyomo for formulation and solution of resilience and operations problems in power grid systems. Furthermore, these tools resulted in several important published results, including the following: The first known global optimization approach that could solve the unit-commitment problem with nonlinear power flow constraints on medium-sized test problems; Improved parallel optimization-based bounds tightening and strengthening of relaxations of AC power flow constraints; and, Optimization-based approaches for improved grid resilience and use of demand response to improve grid resilience with reduction in capital requirements. Result Implications. This project developed first-of-a-kind algorithms for decision-making in critical infrastructure resilience operations and planning, as well as a next-generation toolkit for MINLP researchers. These approaches leveraged high-performance computing architectures to solve some of the largest, most challenging nonlinear discrete optimization problems to global optimality, and these successes were captured in open-source software to enable optimization-based decision-making, and efficient solution of MINLP formulations for electric power transmission grids.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Evaluating Utility-Scale PV-Battery Hybrids in an Operational Model for the Bulk Power System

Systems that combine solar photovoltaic and battery energy storage technologies (PV-BES) are increasingly being proposed and deployed on the bulk power system. The operations and value of PV-BES systems have been extensively studied from the project developer's perspective through analyses that maximize plant-level revenue. However, PV-BES hybrids' operational characteristics are seldom studied from the perspective of bulk power system operators, who seek to optimize the performance of a suite of generation and storage assets that are connected via the transmission network. This work presents modeling approaches for representing and evaluating PV-BES hybrids in a model that optimizes operations across the bulk power system. Its novel contributions include demonstrating a technique to modify a unit commitment and dispatch model to represent the operational synergies of PV-BES hybrids. In particular, we describe the challenges and an approach for representing so-called DC-coupled PV-BES - which utilize a single bi-directional inverter - as a dispatchable resource in a commercial, production cost model (PCM), PLEXOS. We demonstrate this technique in a PCM study of the Los Angeles Department of Water and Power (LADWP) test system, by replacing existing PV and battery generators on the test system with our PV-BES hybrids. We then pursue scenario analysis that is designed to isolate the various drivers of operational strategies for DC-coupled PV-BES hybrids, including the nature of coupling, PV penetration on the system, and varying inverter loading ratios (or degrees of over-sizing of the PV field). Results from the analysis include utilization profiles for the PV DC energy across available pathways, dispatch profiles for the battery component, and the hybrid technologies' impacts on system-wide production costs. The approach presented in this paper can be used in any PCM that is looking to study PV-BES hybrids as a resource in different power system configurations and services.

14 SOLAR ENERGY↗
DOE OSTI2022

Reassessing the Market—Computation Interface to Enhance Grid Security and Efficiency

The goal of this project is to reconsider core market and reliability processes that can potentially yield to transformative advances in power grid security, reliability, and efficiency. Current electric power market designs are strongly a function of computing capabilities and limitations that were available in the mid-to-late 1990s, circa deregulation. This includes constructs such as: (1) a 2-tiered day-ahead/real-time market construct; and (2) linearized (“DC”) real power flow approximations in dispatch and pricing. At that time, state-of-the-art computational capabilities could at the limit address deterministic mixed-integer programming formulations of unit commitment (UC) and linear programming formulations of economic dispatch (ED) at limited fidelity and scale. Such constraints forced limited look-ahead time-horizons, crude approximations of AC power flow physics and operations, and artificial partitioning between day-ahead markets, hour(s)-ahead reliability processes, and real-time markets. Consequently, these limitations have resulted in limited security and reliability with increasing out-of-market payments, particularly as uncertainty associated with renewables and distributed energy resources grows.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Stand-Alone and Hybrid Electric Thermal Energy Storage in the System Advisor Model

This project developed stand-alone electric-thermal energy storage (ETES), stand-alone pumped thermal energy storage (PTES), and hybrid molten-salt power tower (MSPT)-ETES performance and dispatch optimization techno-economic models. The models are available to the public through the System Advisor Model (SAM) software, scripting, and as open-source code. We compared results of the dispatch model to PLEXOS dispatch of a similar generator using the same initial grid pricing signal and found our dispatch model performed well, but closer agreement between the models was limited by the inherent differences between price-taker and unit commitment models. Nevertheless, the price-taker models developed in this project are useful to analyze proposed ETES and PTES technologies because they provide more detailed system and component models, solve several orders of magnitude faster, and are available as free open-source software. The model results represent the most optimistic returns considering grid arbitrage from the input electricity pricing, so the financial results can be applied as a feasibility stage-gate. We also submitted a journal draft paper that describes the ETES dispatch model methodology and demonstrates model functionality. An accepted journal article will serve as peer-reviewed documentation for the models, along with the open-source code, SAM help-menu content, and eventually this final project report.

14 SOLAR ENERGY↗
DOE OSTI2023

Solar Uncertainty Management and Mitigation for Exceptional Reliability in Grid Operations (SUMMER-GO): Project Final Report

The Solar Uncertainty Management and Mitigation for Exceptional Reliability in Grid Operation (SUMMER-GO) project was recently completed through a collaboration among the National Renewable Energy Laboratory, Maxar, the Electric Reliability Council of Texas (ERCOT), the University of Texas at Dallas, the University of California Berkeley, and the University of Colorado Boulder. The project made significant advances in probabilistic solar power forecasting, both through the development of Bayesian model averaging methods for ensemble forecasting and in bringing these and other advancements into practice with Maxar's delivery of operational forecasts to ERCOT. In addition to creating more reliable solar power forecasts, the project developed methods for their utilization in power system operations. These include the development of risk-aware unit commitment and economic dispatch algorithms and methods to reformulate probabilistic forecasts to be used in these power system operational models. Dynamic power system reserve methods were also developed, which have been shown in silico to create economic savings and reliability improvements on an ERCOT-like system as well as financial savings in the ERCOT system through more granular consideration of the uncertainty associated with solar power forecasts. Finally, a situational awareness tool to help grid operators better understand solar power forecast uncertainty in daily operations was developed and extensively vetted.

14 SOLAR ENERGY↗
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↗