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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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The “Assessing the Value and Impact of Dispatchable Concentrating Solar” project consists of three distinct tasks, which examines the role of CSP and other solar plus storage technologies in providing grid services spanning timescales from seconds to decades.
The consortium is conducting cutting-edge collaborative research that will allow advancing solar energy’s role in strengthening the resilience of the U.S. electricity grid. The U.S Department of Energy Solar Technologies Office (SETO) has awarded 4.6 million to Professor Sukumar Kamalasadan (The University of North Carolina at Charlotte) for the project “Optimal Reconfiguration and Resilient Control Framework for Real-Time Photovoltaic Dispatch to Manage Critical Infrastructure (ReDis-PV)”. The project developed a grid management tool that detects cyber and physical threats and forms dynamic clusters to manage photovoltaic and energy optimally. The project collaborating team includes the University of North Carolina at Charlotte, Clemson University, New Mexico State University, Florida International University, Argonne National Laboratory, Idaho National Laboratory, OPAL-RT Corporation, and Duke Energy.
Flexible plant operations and generation (FPOG) offer nuclear power plants (NPPs) the chance to leverage alternative, non-electric revenue streams while ensuring their continued role as reliable, clean, and constant sources of baseload electrical power. The excess thermal energy generated from NPPs during periods of low electricity demand can be channeled as raw materials to numerous industrial processes via a thermal power dispatch (TPD) system. Hydrogen production via high-temperature steam electrolysis (HTSE) is an optimal use case based on technical and economic feasibility. Researchers at Idaho National Laboratory (INL) have conducted previous works that developed and implemented TPD system models within the GSE Solutions Generic Pressurized Water Reactor (GPWR) simulator to support human-in-the-loop (HITL) scenario-based evaluations. The first part of this report documents modifications made to the GPWR TPD model and HMI from the previous iteration in line with a new Sargent and Lundy (S&L) TPD design with an automatic control system. The was done in collaboration with Westinghouse using their three-loop pressurizer water reactor (W3LPWR) simulator which contains an industrial grade automatic control system for the TPD. This was installed in the Human Systems Simulation Laboratory (HSSL) at INL. The second part of the report documents findings from an all-hands-on-deck integration and verification workshop that was conducted in the HSSL over several days. The research team comprised INL human factors and TPD experts, a nuclear engineer from GSE Solutions who implemented the revised TPD model for GPWR, the human-machine interface (HMI) prototyping and human factors team from the University of Idaho, and personnel with operations experience with pressurized water reactors. The workshop provided time and expertise to conduct the final activities to bring the operations, HMI, and simulator into a functional state. The goals of the integration and verification workshop were: 1. to install the revised GPWR TPD model into the HSSL 2. verify the TPD HMI prototype was functional 3. integrate the HTSE Simulink model to GPWR. 3. Issues were identified for resolution, but overall the workshop accomplished its goal to integrated and verify the majority of the intended functional. Future work will resolve the identified issues and use the integrated simulation to support an evaluation and demonstration in the next fiscal year.
This paper presents reduced-order modeling of thermal power dispatch (TPD) from a pressurized water reactor (PWR) for providing heat to nearby heat consuming industrial processes that seek to take advantage of nuclear heat to reduce carbon emissions. The reactor model includes the neutronics of the reactor core, thermal–hydraulics of the primary coolant cycle, and a three-lump model of the steam generator (SG). The secondary coolant cycle is represented with quasi-steady state mass and energy balance equations. The secondary cycle consists of a steam extraction system, high-pressure and low-pressure turbines, moisture separator and reheater, high-pressure and low-pressure feedwater heaters, deaerator, feedwater and condensate pumps, and a condenser. The steam produced by the SG is distributed between the turbines and the extraction steam line (XSL) that delivers steam to nearby industrial processes, such as production of clean hydrogen. The reduced-order simulator is verified by comparing predictions with results from separate validated steady-state and transient full-scope PWR simulators for TPD levels between 0% and 70% of the rated reactor power. All simulators indicate that the flow rate of steam in the main steam line and turbine systems decrease with increasing TPD, which causes a reduction in PWR electric power generation. The results are analyzed to assess the impact of TPD on system efficiency and feedwater flow control. Due to the simplicity of the proposed reduced-order model, it can be scaled to represent a PWR of any size with a few parametric changes. In the future, the proposed reduced-order model will be integrated into a power system model in a digital real-time simulator (DRTS) and physical hardware-in-the-loop simulations.
With the increasing penetration of intermittent renewable energy sources into the grid, there is a growing need for process systems-based strategies that integrate dispatchable and variable energy systems for supplying the demand while maintaining grid reliability. The proposed framework corresponds to a dynamic mixed-integer linear programming optimization approach that integrates coal-fired and natural gas-fired power plants, NaS batteries for energy storage, and solar/wind energy to supply the demand. This optimization approach considers an economic goal and constraints to provide power balance while maintaining the overall damage of the natural gas combined cycle (NGCC) power plant drum under a maximum stress as well as avoiding the overheating of the NGCC superheater and reheater. Renewable curtailment levels are also retained at minimum levels. Case studies are analyzed considering different loads and renewable penetration levels. The results show that the demand was met for all cases. Grid flexibility was mostly provided by the NGCC, while the batteries were used sparingly. In addition, considering a CO 2 equivalent analysis, the environmental performance was intrinsically connected to grid flexibility and the level of renewable penetration. Stress analysis results reinforced the necessity for an equipment health-related constraint.
In this paper, we address the problem of the optimal power dispatch of Distributed Generators (DGs) in Alternating Current (AC) networks, better known as the Optimal Power Flow (OPF) problem. We used, as the objective function, the minimization of power losses (P loss ) associated with energy transport, which are subject to the set of constraints that compose AC networks in an environment of distributed generation. To validate the effectiveness of the proposed methodology in solving the OPF problem in any network topology, we employed one 10-node mesh test system and three radial text systems: 10, 33, and 69 nodes. In each test system, DGs were allowed to inject 20%, 40%, and 60% of the power supplied by the slack generator in the base case. To solve the OPF problem, we used a master–slave methodology that integrates the optimization method Salps Swarm Algorithm (SSA) and the load flow technique based on the Successive Approximation (SA) method. Moreover, for comparison purposes, we employed some of the algorithms reported in the specialized literature to solve the OPF problem (the continuous genetic algorithm, the particle swarm optimization algorithm, the black hole algorithm, the antlion optimization algorithm, and the Multi-Verse Optimizer algorithm), which were selected because of their excellent results in solving such problems. The results obtained by the proposed solution methodology demonstrate its superiority and convergence capacity in terms of minimization of P loss in both radial and mesh systems. It provided the best reduction in minimum P loss in short processing times and showed excellent repeatability in each test system and scenario under analysis.
This presentation provides a high-level overview of utility-scale PV-wind storage plants for dispatchability and reliability services.
Stationary fuel cells can generate both electrical power and heat, making it them a promising local source to serve building loads. In this paper, we develop an optimal energy dispatch controller to operate a fuel cell-integrated building. The controller leverages the inherent thermal in the building to lower reduce its operating cost and allows the building to participate in providing grid services. The proposed controller is tested in a co-simulation setup, where the real building is represented as a high-fidelity model in EnergyPlus.
Presentation on the impact of storage dispatch assumptions on resource adequacy assessment.
The presentation, part of the Solar-Derived Hydrogen: Understanding and Implementing the Cost Reduction Drivers Panel at the North America Smart Energy Week 2019, discusses considerations for using low-cost, dispatch-constrained electricity for hydrogen production.
A significant difficulty associated with the use of stochastic programming to solve optimal power flow problems on a 5-minute timescale is the quality of renewable energy scenarios input by the user. This is especially true when considering power systems with high penetrations of renewable energy, e.g. wind power. This paper introduces the use of stochastic programming to solve the DC optimal power flow problem with scenarios drawn directly from high-fidelity data sets. Hence, the proposed method avoids the problem of lost physics by finding high-fidelity analogs that can describe future states of the system. Furthermore, this method can be simply extended to output multi-period scenarios to the stochastic program. We demonstrate the effectiveness of this technique by simulating dispatch operations on a synthetic test system over the course of a week.
Renewable energy technologies, specifically, solar photovoltaic cells, combined with battery storage and diesel generators, form a hybrid system capable of independently powering remote locations, i.e., those isolated from larger grids. If sized correctly, hybrid systems reduce fuel consumption compared to diesel generator-only alternatives. We present an optimization model for establishing a hybrid power design and dispatch strategy for remote locations, such as a military forward operating base, that models the acquisition of different power technologies as integer variables and their operation using nonlinear expressions. Our cost-minimizing, nonconvex, mixed-integer, nonlinear program contains a detailed battery model. Due to its complexities, we present linearizations, which include exact and convex under-estimation techniques, and a heuristic, which determines an initial feasible solution to serve as a “warm start” for the solver. We determine, in a few hours at most, solutions within 5% of optimality for a candidate set of technologies; these solutions closely resemble those from the nonlinear model. Lastly, our instances contain real data spanning a yearly horizon at hour fidelity and demonstrate that a hybrid system could reduce fuel consumption by as much as 50% compared to a generator-only solution.
We embellish a mixed-integer program that prescribes a set of renewable energy, conventional generation, and storage technologies to procure, along with a corresponding dispatch strategy. Specifically, we add combined heat and power to this set. The model minimizes fixed and operational costs less incentives for the use of various technologies, subject to a series of component interoperability and system-wide constraints. The resulting mixed-integer linear program contains hundreds of thousands of variables and constraints. We demonstrate how to efficiently formulate and solve the corresponding instances such that we produce near-optimal solutions in minutes. A previous rendition of the model required hours of solution time for the same instances.
As utility electricity rates evolve, pairing solar photovoltaic (PV) systems with battery storage has potential to ensure the value proposition of residential solar by mitigating economic uncertainty. In addition to batteries, load control technologies can reshape customer load profiles to optimize PV system use. The combination of PV, energy storage, and load control provides an integrated approach to PV deployment, which we call 'solar plus'. The U.S. National Renewable Energy Laboratory's Renewable Energy Optimization (REopt) model is utilized to evaluate cost-optimal technology selection, sizing, and dispatch in residential buildings under a variety of rate structures and locations. The REopt model is extended to include a controllable or 'smart' domestic hot water heater model and smart air conditioner model. We find that the solar plus approach improves end user economics across a variety of rate structures - especially those that are challenging for PV - including lower grid export rates, non-coincident time-of-use structures, and demand charges.
Geothermal power plants often deploy less than their full power capacity due to declining geothermal resources. Integrating heat from a concentrating solar power (CSP) collector field increases the power output at low cost. This article considers five methods of solar heat addition in a double-flash geothermal plant. The most promising solution converts solar heat into electrical work with an efficiency of 24.3%. The economic feasibility and optimal sizing of the solar field and thermal stores are evaluated. A hybrid plant that increases power generation from 22 to 24 MWe has a Levelized Cost of Electricity (LCOE) of 0.07 +/- 0.01 $/kWhe. Adding three hours of storage increases the LCOE to 0.08 +/- 0.01 $/kWhe. Photovoltaic systems are considered to be a low-cost renewable technology, but an equivalent photovoltaic system with battery storage costs 0.15 +/- 0.07 $/kWhe due to the high cost and replacement rate of batteries compared to thermal storage. The hybrid plant also has a lower LCOE than a conventional CSP plant. If the dispatchability that thermal storage provides is rewarded with higher electricity prices, calculations indicate storage becomes an attractive investment when discharged power receives 1.75 times the typical price of electricity.
The traditional implementation of cool thermal energy storage (CTES) must be reimagined within the context of a dynamic grid and smart buildings operating as connected communities. As most buildings do not operate central chillers or connect to district cooling loops, this necessitates a broader use of packaged CTES. Our objective is to begin answering the question of how such packaged CTES should be implemented within a connected community. We do so by presenting a simulation–optimization workflow employing building energy modeling software and a mixed-integer linear program to design and dispatch a packaged CTES technology to achieve minimum total annual cost. We demonstrate this methodology on a seven-building case study using current utility rates and find that total annual cooling energy costs can be reduced by 17.8% compared to baseline, after accounting for the cost of storage. We perform three parametric sensitivity studies to evaluate modeling assumptions and obtain the prioritization of storage procurement as a function of annualized life-cycle cost of storage. We find that a community optimization approach provides significantly different results than individual building optimizations and provides greater savings compared to baseline.
Geothermal systems making use of advanced drilling and well stimulation techniques have the potential to provide tens to hundreds of gigawatts of clean electricity generation in the United States by 2050. With near-zero variable costs, geothermal plants have traditionally been envisioned as providing “baseload” power, generating at their maximum rated output at all times. However, as variable renewable energy sources (VREs) see greater deployment in energy markets, baseload power is becoming increasingly less competitive relative to flexible, dispatchable generation and energy storage. Herein we conduct an analysis of the potential for future geothermal plants to provide both of these services, taking advantage of the natural properties of confined, engineered geothermal reservoirs to store energy in the form of accumulated, pressurized geofluid and provide flexible load-following generation. We develop a linear optimization model based on multi-physics reservoir simulations that captures the transient pressure and flow behaviors within a confined, engineered geothermal reservoir. We then optimize the investment decisions and hourly operations of a power plant exploiting such a reservoir against a set of historical and modeled future electricity price series. Further, we find that operational flexibility and in-reservoir energy storage can significantly enhance the value of geothermal plants in markets with high VRE penetration, with energy value improvements of up to 60% relative to conventional baseload plants operating under identical conditions. Across a range of realistic subsurface and operational conditions, our modeling demonstrates that confined, engineered geothermal reservoirs can provide large and effectively free energy storage capacity, with round-trip storage efficiencies comparable to those of leading grid-scale energy storage technologies. Optimized operational strategies indicate that flexible geothermal plants can provide both short- and long-duration energy storage, prioritizing output during periods of high electricity prices. Sensitivity analysis assesses the variation in outcomes across a range of subsurface conditions and cost scenarios.
Microgrids are an increasingly popular solution to provide energy resilience in response to increasing grid dependency and the growing impacts of climate change on grid operations. However, existing microgrid models do not currently consider the uncertain and long-term impacts of climate change when determining a set of design and operational decisions to minimize long-term costs or meet a resilience threshold. In this paper, we develop a novel scenario generation method that accounts for the uncertain effects of (i) climate change on variable renewable energy availability, (ii) extreme heat events on site load, and (iii) population and electrification trends on load growth. Additionally, we develop a two-stage stochastic programming extension of an existing microgrid design and dispatch optimization model to obtain uncertainty-informed and climate-resilient energy system decisions that minimizes long-term costs. Use of sample average approximation to validate our two case studies illustrates that the proposed methodology produces high-quality solutions that add resilience to systems with existing backup generation while reducing expected long-term costs.