Search NASA⌕ Search

SEARCH · Search NASA

Results for “dispatch”

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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Optimal Sizing and Dispatch of Solar Power with Storage

Designers of utility-scale solar plants with storage, seeking to maximize some aspect of plant performance, face multiple challenges. In many geographic locations, there is significant penetration of photovoltaic generation, which depresses energy prices during the hours of solar availability. An energy storage system affords the opportunity to dispatch during higher-priced time periods, but complicates plant design and dispatch decisions. Solar resource variability compounds these challenges, because determining optimal system sizes requires simultaneously considering how the plant will be operated under the imposed market and weather conditions. We develop an approach to analyze the economic performance of hybrid and single-technology solar power plants, which incorporates optimal dispatch, and considers the expected electricity market and weather conditions. We utilize the System Advisor Model software package to simulate the operation of multiple renewable generation and energy storage technologies, in conjunction with hourly-fidelity generation decisions determined by a revenue-maximizing, mixed-integer linear program. We show that, under our assumed market and weather conditions, the lifetime benefit-to-cost ratio can be improved by 6 to 19 percent, relative to a baseline design without optimizing, and that a concentrating solar power with thermal energy storage design produces significantly more energy per year, but is less profitable under our cost assumptions.

black-box optimization↗
DOE OSTI2023

A hybrid data-driven and model-based approach for computationally efficient stochastic unit commitment and economic dispatch under wind and solar uncertainty

Stochastic unit commitment (UC) and economic dispatch (ED) are imperative in dealing with uncertainty in renewable forecast for power system operation and planning such that the overall expected production cost is minimized over the planning horizon. However, accurate calculation of the expected production cost requires assessment of a very large number of different scenarios of uncertain renewable resources, such as solar and wind, which is practically infeasible to simulate in real time. This article proposes a hybrid datadriven and physics-based model-predictive paradigm to efficiently solve for stochastic unit commitment and economic dispatch considering uncertainty in wind and solar power forecasts. Here, the novelty of the approach lies in decoupling the production cost estimation from the unit commitment and economic dispatch optimization problems under uncertainty without compromising on the fidelity of the solutions. A data-driven machine learning model is first developed to predict the mean optimal production cost. A physics-based inverse problem is then solved to get the stochastic UC and ED profiles from the expected cost. The presented approach considers, for the first time, solar uncertainty in UC/ED determination and enables efficient and accurate propagation of wind and solar uncertainty to estimate the statistics of the production cost. The effectiveness of the developed approach is demonstrated systematically on a stylized RTS-GMLC single-node system. The overall framework predicts the expected cost 62.5% more accurately than the existing state-of-the-art, on unforeseen days during the entire year, and yields, for the first time, the associated physically consistent UC and ED profiles. The solutions are also shown to be flexible in providing adequate daily reserves to address any statistical deviations from probabilistic power forecasts. The computational time associated with the presented method is only about 10 s compared to over 24 h needed for a conventional stochastic UC/ED determination under uncertainty on an Intel Core i9 processor with 32 GB of RAM.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Economic dispatch of offshore renewable energy resources for islanded communities with optimal storage sizing

Coastal or isolated microgrids depend on diesel generators and could benefit from renewable energy resources, especially offshore wind and wave energy. Integrating these resources into microgrids is complicated by their high intermittency, which requires optimal economic dispatch to effectively evaluate. This study considers three coastal or islanded sites, and uses mid-fidelity models of wind and wave energy technologies, and local demand data to solve the optimal economic dispatch problem. An optimal storage sizing method is developed that finds the smallest capacity of energy storage required to meet the microgrid load during each season. The storage capacity decreases by a factor of two at most when adding wave energy converters to a system. Adding wave energy converters to a farm decreases cost by about 30%. Furthermore, the required storage size varies by two to three times from summer to winter. Compared with the state-of-the-art approaches that often overlook realistic offshore renewable energy technology in microgrid economic dispatch and optimal storage sizing, the proposed solution introduced in this study allows for better site selection, microgrid design, converter selection, and storage sizing considerations for isolated microgrids.

16 TIDAL AND WAVE POWER↗
DOE OSTI2021

Economic Comparison Between Battery and Supercapacitor for Hourly Dispatching Wave Energy Converter Power

This paper demonstrates a dispatching scheme of slider-crank wave energy converter (WEC) power generation using two different kinds of energy storage components, namely, Lithium-ion (Li-ion) battery and Supercapacitors (SC). The performance of the two energy storage components has been compared in order to develop the most economical energy storage system for WEC hourly dispatching scheme. The cost optimization of the energy storage system considering both cycling and calendar aging expenses is made based on its usage of depth of discharge. In this study, extensive simulation is conducted in MATLAB/Simulink platform, and it is found that SC is a better solution than Li-ion battery in terms of economic assessment for hourly dispatching WEC power.

50 EE - Wind and Water Power Program - Water (EE-4↗
DOE OSTI2021

Visualization of Multi-Fidelity Approximations of Stochastic Economic Dispatch

As renewable energy generation deployment increases, the operation of electrical grids becomes more complex. Economic dispatch is part of a grid operator's regular decision process where the amount of energy to generate is determined based on the number of available generators and the actual level of energy demand. Renewable generators are inherently stochastic due to the chaotic nature of weather patterns, and thus, real-time decisions of economic dispatch become increasingly complex. Modeling efforts to assist in these decisions in the highest fidelity typically take hours to days to solve on leadership-class computers; too long for the 5-minute operational time-frame demanded of operators. Alternatively, multi-fidelity approximations can be used to predict generation levels quickly and with sufficient accuracy to be used for real-time operations. We have developed a visualization tool to demonstrate the utility of multi-fidelity approximations by displaying contextual results of economic dispatch approximations, comparisons across fidelity levels of generation levels and possible failures to meet demand, and meta-data on the modeling setup.

interactive visualization↗
DOE OSTI2021

Development and Demonstration of a Reduced-Order PWR Power Dispatch Simulator

This report describes the development, modeling, and results of a reduced-order thermal power dispatch pressurized water reactor (RO-TPD-PWR) power plant simulator that incorporates coupled electrical and thermal power dispatch to an industrial process. The simulator is built on models that have been developed in previous work, including a Rancor Microworld model previously developed by INL and the University of Idaho [Ref] and reactor core and secondary system models that have been published previously [Ref]. The simulator also includes a scalable model of a high temperature electrolysis (HTE) system that has been developed at INL. A particular benefit of the Rancor Microworld model is that it is compatible with a human-system interface (HMI) that has already been developed by the Human System Simulation Laboratory (HSSL) at INL that can emulate the control room of a PWR for realistic human-in-the-loop studies with mock nuclear power plant operating procedures. The results of these human-in-the-loop tests will be compared with similar tests using a commercial full-scope generic PWR (GPWR) simulator that has been modified to include thermal power dispatch, referred to as the TPD-GPWR [ , ]. The simplicity of the RO-TPD-PWR helps to better understand and verify the results of the TPD-GPWR, which employs >1,000 variables and has much greater complexity. A second key purpose of the RO-TPD-PWR simulator is that its relative simplicity allows it scale between lab-size equipment (~100 kW) and full-scale nuclear power plants (~1 GW) by adjusting only a few parameters in the models, such as fluid masses and heat exchanger areas. The RO-TPD-PWR simulator will be used to guide the controls of lab tests, so that equipment in those tests, such as the Thermal Energy Distribution System (TEDS) and pilot-scale high temperature electrolysis (HTE) units at scales of 50-250 kW, may operate in a manner that is relevant to full-scale PWR operations.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Research Highlight - Dispatch Optimization, System Design and Cost Benefit Analysis of a Nuclear Reactor with Molten Salt Thermal Storage

We highlight our work from our previous publication titled "Dispatch Optimization, System Design, and Cost Benefit Analysis of a Nuclear Reactor with Molten Salt Thermal Storage" published in MDPI Energies 2022 (doi.org/10.3390/en15103599). Variable renewable energy availability has increased the volatility in energy prices in most markets. Nuclear power plants, with a large ratio of capital to variable costs, have historically operated as base load energy suppliers but the need for more flexible operation is increasing. We simulate the techno-economic performance of a 950 MWt nuclear power plant, based on the Westinghouse lead-cooled fast reactor, coupled with molten salt thermal storage as a method for flexible energy dispatch. We use the System Advisor Model to model the nuclear reactor thermal power input and power cycle operating modes. We combine this robust engineering model with a mixed-integer linear program model for optimized dispatch scheduling. We then simulate the coupled nuclear and thermal storage system under different market scenarios with varying price volatility. We find that the coupled plant outperforms the base plant under markets where energy price peaks fluctuate by a factor of two or more about the mean price. We show that a calculated power purchase agreement price for the plant improves by up to 10% when operating under California energy market conditions. Sensitivity analysis on the thermal storage cost shows that the optimal design remains unchanged even when doubling costs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Chance-constrained economic dispatch with renewable energy and storage

Increased penetration of renewables, along with uncertainties associated with them, have transformed how power systems are operated. High levels of uncertainty means that it is not longer possible to guarantee operational feasibility with certainty, instead constraints are required to be satisfied with high probability. We present a chance-constrained economic dispatch model that efficiently integrates energy storage and high renewable penetration to satisfy renewable portfolio requirements. Specifically, it is required that wind energy contributes at least a prespecified ratio of the total demand and that the scheduled wind energy is dispatchable with high probability. We develop an approximated partial sample average approximation (PSAA) framework to enable efficient solution of large-scale chanceconstrained economic dispatch problems. Computational experiments on the IEEE-24 bus system show that the proposed PSAA approach is more accurate, closer to the prescribed tolerance, and about 100 times faster than sample average approximation. Improved efficiency of our PSAA approach enables solution of WECC-240 system in minutes.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Integration of optimal operational dispatch and controller determined dynamics for microgrid survivability

We present that the reliability and resilience of the electrical power grids are essential to industry, economy and society. Microgrids that are able to island from the bulk electrical grid are one technology that may vastly improve electrical power service to customer loads. To achieve these improvements, an islanded microgrid should be able to operate through the loss of one of its generators without shedding electrical load. The loss of one generator will typically result in significant additional loads, including transient overloads, being placed on the remaining generators. There is also the possibility of additional generator tripping during these processes (i.e. cascading failures), which would likely result in the collapse of the microgrid. The novelty of our work consists in incorporating dynamic models of generator controllers into a microgrid optimal dispatch formulation with the ultimate goal to avoid operational failures and ensure the “survivability” of all-inverter microgrids to generator loss and transient overloads. The integration of generator and controller dynamics into the optimal dispatch formulation significantly increases the computational complexity. As we develop algorithms to restore speed of the optimization, our method can be readily implemented into a new operational strategy capable of an unprecedented level of reliability against generator contingencies. In addition, we quantitatively illustrate the effect of the survivability constraints on the microgrid operating costs and how the related trade-off between capital and operating costs should be taken into account at the design stage. Finally, the methods developed here also apply to the dispatch of off-grid microgrids.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Dispatch optimization of a concentrating solar power system under uncertain solar irradiance and energy prices

The integration of thermal energy storage into a concentrating solar power system allows for mitigating some of the risk associated with uncertain solar irradiance and uncertain energy prices. We solve a 48 h dispatch optimization model with continually updated conditional point forecasts of both direct normal irradiance (DNI) and electricity prices with a rolling-horizon scheme at hourly resolution over the course of a year. Joint, conditional forecasts for DNI and prices are formed using an autoregressive moving-average time series model with exogenous weather predictors. We guide dispatch using a mixed-integer programming model, but in order to evaluate performance we use the System Advisor Model (SAM) of the National Renewable Energy Laboratory. SAM is a techno-economic simulation model that accounts for plant thermodynamics with higher fidelity. Our conditional DNI forecasts improve annual revenue by 4%–12% over using historical forecasts based on data from previous years. Conditional price forecasts improve annual revenue by 6%–19% in the real-time market over analogous historical forecasts. Updating these forecasts every six hours, rather than every 24 h, further improves annual revenue by 5%–6%. Here, we also investigate a method that values terminal inventory in our dispatch optimization model, again when used in a rolling-horizon scheme.

14 SOLAR ENERGY↗
DOE OSTI2018

Policies to keep and expand the option of concentrating solar power for dispatchable renewable electricity

Concentrating solar power (CSP) is one of the few renewable electricity technologies that can offer dispatchable electricity at large scale. Thus, it may play an important role in the future, especially to balance fluctuating sources in increasingly renewables-based power systems. Today, its costs are higher than those of PV and wind power and, as most countries do not support CSP, deployment is slow. Unless the expansion gains pace and costs decrease, the industry may stagnate or collapse, and an important technology for climate change mitigation has been lost. Keeping CSP as a maturing technology for dispatchable renewable power thus requires measures to improve its short-term economic attractiveness and to continue reducing costs in the longer term. We suggest a set of three policy instruments - feed-in tariffs or auctions reflecting the value of dispatchable CSP, and not merely its cost; risk coverage support for innovative designs; and demonstration projects - to be deployed, in regions where CSP has a potentially large role to play. This could provide the CSP industry with a balance of attractive profits and competitive pressure, the incentive to expand CSP while also reducing its costs, making it ready for broad-scale deployment when it is needed.

14 SOLAR ENERGY↗
DOE OSTI2021

A framework for integrated dispatching and charging management of an autonomous electric vehicle ride-hailing fleet

The convergence of electrification and automated driving will introduce opportunities to improve the operation and energy-efficiency of transportation systems. This paper discusses the challenges of dispatching autonomous electric vehicles (AEVs) in a ride-hailing fleet and their interactions with charging infrastructure. An integrated decision-making framework for dispatching and charging has been proposed using system optimization approaches. An agent-based platform has been developed for simulating and testing the proposed methods. A case study using New York City taxi data has been performed with different fleet sizes, dispatching strategies, and charging networks. Advantages of optimization-based approaches for AEV fleet management have been studied and demonstrated, for example, for a fleet of 1,750 AEVs to meet 100,000 daily requests, optimization-based centralized fleet management would result in 14% more ride requests satisfied and 43% fewer zero-occupancy miles traveled than if AEVs make independent decisions based on heuristic strategy. Benefits on reducing fleet size and charging downtime from optimization approaches are also comprehensively illustrated.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Black-Box Optimization for Design of Concentrating Solar Power and Photovoltaic Hybrid Systems with Optimal Dispatch Decisions

The hybridization of concentrating solar power (CSP) and photovoltaics (PV) can enable dispatchable renewable electricity generation at a lower price than current stand-alone CSP systems. However, designing a CSP-PV hybrid system can be challenging because of the many degrees of freedom in design that affect the internal and external system interactions and trade-offs. We develop a methodology to determine optimal designs for CSP-PV hybrids by implementing NLopt's derivative-free, or “black-box,” algorithms around pre-existing CSP-PV hybrid simulation software that utilizes the National Renewable Energy Laboratory’s System Advisor Model (SAM); we then employ a dispatch optimization model to determine operational decisions that maximize a plant’s profits. We present optimal designs for CSP-PV hybrid systems dispatching against four time-of-delivery (ToD) pricing structures. NLopt’s algorithms can improve the base case design’s power purchase agreement (PPA) price by 15% to 21%, depending on the ToD pricing structure. In addition, we present the resulting optimal CSP-PV hybrid design’s annual performance metrics, which tend to have capacity factors between 50% and 62%, but are able to generate electricity during the year’s highest-valued periods about 90% of the time. Lastly, we investigate the trade-offs between capacity factor and PPA price using Pareto fronts and demonstrate that, for some ToD pricing structures, the system capacity factor can increase by 20% but at the expense of a 2% increase in PPA price.

black-box↗
DOE OSTI2019

Loss Minimization With Optimal Power Dispatch in Multi-Frequency HVac Power Systems

Low-frequency high-voltage ac transmission scheme has recently been proposed as an alternative approach for bulk power transmission. This paper proposes a multi-period optimal power flow (OPF) for a multi-frequency HVac transmission system that interconnects both conventional 50/60-Hz and low-frequency grids using back-to-back converters with a centralized control scheme. The OPF objective is to minimize system losses by determining the optimal dispatch for generators, shunt capacitors, and converters. The OPF constraints include the operational constraints of all HVac grid and converter stations. The resulting mixed-integer nonlinear programing problem is solved using a proposed framework based on the predictor-corrector primal-dual interior-point method. The proposed OPF formulation and solution approach are verified using a multi-frequency HVac transmission system that is modified from the IEEE 57-bus system. Here, the results with the optimal dispatch from the proposed method during a simulated day show a significant loss reduction and an improved voltage regulation compared to those when an arbitrary dispatch is chosen.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Optimal Energy Dispatch Controller for Fuel Cell-Integrated Multi-Zone Building

Stationary fuel cells provide potential opportunities for energy savings when integrated with buildings. Through smart dispatch of both electrical power and heat generated by the fuel cells and managing the building loads, the buildings can achieve more efficient operation. In this paper, we develop an optimal energy dispatch controller to operate a fuel cell-integrated building. The controller leverages the inherent thermal storage and the dispatchable fuel cell to reduce its operating cost and to allow the building to participate in grid services. The proposed controller is implemented on two types of commercial buildings, a large office building and a large hotel, and the effectiveness of the controller is demonstrated through simulations. The results also indicate that the potential saving varies significantly with different system parameters, including season, fuel prices, and equipment sizing, which provide helpful insights for building operators and other stake holders.

39 EE - Hydrogen and Fuel Cell Technologies (EE-3F↗
DOE OSTI2024

Dispatch Optimization Variable Engine

The Dispatch Optimization Variable Engine (DOVE) is software tool written in python, developed at Idaho National Laboratory (INL) that provides an easily accessible application-programming-interface (API) to performing resource dispatch optimization analysis for integrated energy system (IES) configurations. DOVE is an integral part of the Framework for Optimization of Resources and Economics (FORCE) software suite and is leveraged by codes such as the Holistic Energy Resource Optimization Network (HERON) and the Optimization of Real-Time Capacity Allocation (ORCA). The philosophy behind DOVE is to provide a modular software solution to IES planning and operation by utilizing state-of-the-art algorithms and machine learning. The goal is to accurately capture the dispatching behavior of a complex energy system given varying time-dependent signals for demand and commodity pricing.

McDowell, DylanJ. [Idaho National Laboratory (INL)↗
DOE OSTI2026

A Comprehensive Comparison of Methods for Evaluating Dispatch of Long-Duration Energy Storage in Power Systems Models

Long-duration energy storage (LDES) could play a pivotal role in the transformation of electricity grids with high shares of variable renewable energy (VRE) such as solar and wind. However, the weather-dependent nature of VRE introduces challenges for grid balancing and stability, which LDES - along with short-duration energy storage (SDES) - can help address. However, modeling LDES in production cost models (PCMs) is particularly challenging due to the need for high temporal resolution over extended optimization windows while preserving chronology, which ensures the alignment of energy storage operations with VRE generation over multi-day periods. This report compares traditional dispatch methods with advanced LDES dispatch strategies, such as the extended horizon approach, across different PCM platforms and examines tradeoffs and scalability. The comparison reveals that the traditional 1-day optimization horizon within the PCM leads to inefficient utilization of LDES. In contrast, extending the optimization horizon as much as possible significantly reduces curtailment and improves storage dispatch, especially in renewable-dense systems. There is also promise in using state-of-charge or end volume targets set by an external model, however this requires an additional modeling set and generally increases computational burden. This paper presents a comparison of these various methods in a number of power systems, showing algorithms initially in small test systems and scaling up to large, country-wide simulations. Overall, the research presents the trade-offs of various computational methods and illustrates how LDES may play an essential role in power systems of the future.

14 SOLAR ENERGY↗
DOE OSTI2022

Simulation of 15% and 50% Thermal Power Dispatch to an Industrial Facility Using a Flexible Generic Full-Scope Pressurized Water Reactor Plant Simulator

Nuclear power plants in the United States are increasingly challenged to compete in wholesale electricity markets due to the low electricity costs and increasingly dynamic grid conditions from competing generation sources. An alternative market for nuclear power is industrial facilities that can use the thermal and/or electrical power generated by a nuclear power plant to offset the economic losses incurred by electricity market challenges. A generic pressurized water reactor (PWR) simulator was used to show the results of a basic design for a generic thermal power extraction system and tests were run using a set of procedures to show what happens when a nuclear power plant transitions from full electrical power dispatch to 15% and 50% thermal power dispatch. This type of operation leads to losses in turbine performance efficiency due to the deviation from the design operating point, but because the thermal power is also used by the industry load without conversion losses, the combined thermal efficiency of the PWR increases. For the 15% case, the thermal efficiency increased from 32% to 41.9%, while for the 50% case, the efficiency increased up to 60.1%. In addition, for 50% thermal power dispatch, the power dissipated by the condenser decreased from approximately 2000 to approximately 1300 MW (thermal), indicating a substantially diminished impact on the environment in terms of releasing heat into the cooling water reservoir.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗