Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Cost optimization”

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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Cost-optimized energy storage operation for a grid-connected solar PV system at community and individual scales

This study provides a comparative analysis of grid-connected PV-integrated battery storage at individual and community scales. The paper addresses the challenge of managing energy demand-generation mismatch by using a battery energy storage optimization algorithm, which minimizes operational costs while accounting for battery degradation. Also, this work introduces a broader evaluation basis that includes seasonal variability, grid exchange smoothness, and scalability across different battery capacities. Results show that community-scale storage more effectively dampens grid exchange power fluctuations and reduces system costs, particularly with moderate price differences between electricity buying and selling prices and low battery capacities. The paper also analyzes the impacts of static control versus cost-optimized battery system management. Here, it is shown that the gap in system costs between the cost-optimized and static control scenarios widens as the price difference increases.

25 ENERGY STORAGE↗
DOE OSTI2024

Deep Reinforcement Learning for Microgrid Cost Optimization Considering Load Flexibility

This paper proposes a novel Soft-Actor-Critic (SAC) based Deep Reinforcement Learning (DRL) method for optimizing the cost of microgrid operation by leveraging load flexibility. The proposed SAC-DRL method is designed to coordinate the control of distributed energy resources (DERs) and flexible load, addressing practical energy billing formation by power distribution utilities. Key contributions include an innovative reward function to mitigate sparse reward challenges and a mixed control strategy for discrete and continuous variables, ensuring radial network topology and minimizing power loss. We evaluate the proposed method on the model of a real microgrid located in Southern California, U.S.. The SAC-DRL model is tested to demonstrate its efficacy in reducing grid dependence, optimizing resource use, and minimizing costs. The results highlight the potential of DRL in modern energy systems, offering a sustainable and economically efficient solution for energy management in microgrids.

deep reinforcement learning↗
DOE OSTI2025

Scalable Truck Charging Demand Simulation for Cost-Optimized Infrastructure Planning

This project developed a scalable, high-resolution model to simulate medium- and heavy-duty (MHD) electric truck charging demand and assess its impact on grid infrastructure. Using generative modeling, simulation, and cost optimization, the project delivered an end-to-end software pipeline and a library of 96 real-world scenarios for the Dallas–Houston megaregion. We demonstrated a modular architecture for transportation and grid modeling, implemented cost-optimized infrastructure planning methods, and quantified grid capital, operational, and environmental costs across a wide range of truck electrification scenarios. The results have been adopted by major utility stakeholders and contributed to regional planning efforts.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Least-cost Optimal Distribution Grid Expansion (LODGE): Utility Pilots

The Least-cost Optimal Distribution Grid Expansion (LODGE) model provides the optimal portfolio of distribution system upgrades—e.g., voltage regulators, feeder reconductoring, transformer upgrades and non-wires alternatives (NWA), such as strategic siting of storage and distributed generation—to interconnect distributed energy resources (DERs) and enable load growth. It can be used to assess grid infrastructure costs and explore policy and regulatory solutions for distribution planning and DER valuation.In 2025, Berkeley Lab conducted three pilot analyses to validate LODGE results with empirical utility data before the model’s first release in 2026. The pilots, done with utilities in Washington, Colorado, and New Mexico, provide examples that illustrate how the model works, what it can do, and the value of the analysis.

Heleno, Miguel↗
DOE OSTI2024

Evaluating Property Model Accuracy for Cost Optimization of Desalination Technologies

Conference presentation conveying work conducted on property modeling and implications on cost optimization of seawater desalination technology. The authors assess the accuracy of four different property modeling approaches to represent thermodynamic properties of seawater. These properties are used with process flowsheets that include either reverse osmosis or mechanical vapor compression. The accuracy of property estimation is related to deviations in process design and levelized cost.

Sakhai, Savannah↗
DOE OSTI2024

Cost-optimal selection of pH control for mineral scaling prevention in high recovery reverse osmosis desalination

Conference proceedings for Foundations of Computer Aided Process Design (FOCAPD) 2024 that details a cost optimization model for pH control to prevent mineral scaling in high recovery reverse osmosis. This work applies a framework for incorporating detailed water chemistry predictions such as precipitation, pH change, and mineral scaling tendencies with process-scale optimization. It demonstrates that the acid choice for pH control can have a significant impact on pretreatment costs.

Amusat, Oluwamayowa↗
DOE OSTI2024

An Evaluation of the Effect of Network Cost Optimization for Leadership Class Supercomputers

Dragonfly-based networks are an extensively deployed network topology in large-scale high-performance computing due to their cost-effectiveness and efficiency. The US will soon have three Exascale supercomputers for leadership class workloads deployed using dragonfly networks. Compared to indirect networks of similar scale, the dragonfly network has considerably reduced cable lengths, cable counts, and switch counts, resulting in significant network cost savings for a given system size, however, these cost reductions result in reduced global minimal paths and more challenging routing. Additionally, large scale dragonfly networks often require a taper at the global link level, resulting in less bisection bandwidth than is achievable in other traditional non-blocking topologies of equivalent scale. While dragonfly networks have been extensively studied, they have yet to be fully evaluated in an extreme scale (i.e., exascale) system that targets capability workloads. In this paper, we present the results of the first large scale evaluation of a dragonfly network on an exascale system (Frontier) and compare its behavior to a similar scale fat-tree network on a previous generation TOP500 system (Summit). This evaluation aims to determine the effect of network cost optimizations by measuring a tapered topology’s impact on capability workloads. Our evaluation is based on a collection of synthetic microbenchmarks, mini-apps, and full scale applications. It compares the scaling efficiencies of each benchmark between the dragonfly-based Frontier and the fat-tree-based Summit systems. Our results show that a dragonfly network is $\sim \mathbf{3 0 \%}$ more cost efficient than a fat-tree topology, which amortizes to $\sim 3 \%$ of an exascale system cost. Furthermore, while tapered dragonfly networks impose significant tradeoffs, the impacts are not as broad as initially thought and are mostly seen in applications with global communication patterns, particularly all-to-all (e.g., FFT-based algorithms), but also local communication patterns (e.g., nearest-neighbor algorithms) that are sensitive to network performance variability.

Khan, Awais↗
DOE OSTI2023

Cost optimization of low-salt-rejection reverse osmosis

Low-salt-rejection reverse osmosis (LSRRO) is an emerging membrane-based desalination technology for concentrating brines with potentially lower energy consumption and cost than thermally driven processes. We assess the technoeconomic performance of LSRRO by using an optimization model that minimizes the levelized cost of water (LCOW). We develop detailed models for the RO module and other equipment using WaterTAP (Water treatment Technoeconomic Assessment Platform), an open-source simulation and optimization software for water treatment. We improve upon previous LSRRO cost optimization studies by optimizing the salt permeability for each stage, which has significant implications for technoeconomic performance. We present the cost-optimal design, operation, and performance for three cases: 35 g/L TDS feed at 70% recovery, 70 g/L at 55%, and 125 g/L at 35%. The cost-optimal LCOW and specific energy consumption (SEC) for these cases are 0.70, 1.89, and 7.41 $/m 3 and 3.9, 8.4, and 30.4 kWh/m 3 , respectively. For each case, we use sensitivity analyses to provide guidance on key factors that impact economic viability. We also present the cost-optimal LCOW and SEC for feed concentrations between 5 and 220 g/L TDS and recoveries between 30 and 90%. So, we compare LSRRO to other high-salinity desalination technologies and find that it may be cost-competitive below feed concentrations of 125 g/L.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Assessing the Accuracy of Property Model Predictions for Cost Optimization of Desalination Technologies

Accurate modeling of seawater thermophysical and thermodynamic properties is critical for optimizing desalination processes. This study compares three seawater property models, a Reaktoro multicomponent model, the thermophysical seawater properties library from the Massachusetts Institute of Technology, and a simplified sodium chloride model, in the context of levelized cost of water (LCOW) minimization for reverse osmosis (RO) and mechanical vapor compression systems. Process simulations and cost optimizations reveal that although all three models yield comparable LCOW and specific energy consumption (SEC) estimates under baseline conditions, deviations among their predictions increase with salinity. Relative differences in LCOW and SEC reach up to 6% and 8%, respectively. RO results show greater variability due to differences in osmotic pressure predictions, which affect pressure constraints at high recoveries. Computational performance varies substantially; specifically, Reaktoro simulations are up to 28 times slower than empirical models due to their detailed equilibrium calculations. These results suggest that empirical models offer acceptable accuracy for routine desalination process design, while Reaktoro provides advantages in scenarios requiring detailed speciation, such as scaling or pH adjustment studies. These findings underscore the importance of selecting appropriate property models based on the modeling objective of desalination applications and motivate future work integrating thermodynamic rigor with empirical efficiency.

Physical and chemical properties↗
DOE OSTI2024

Least-cost Optimal Distribution Grid Expansion (LODGE) v1

LODGE finds the least-cost portfolio of traditional distribution system upgrades to integrate new loads and behind-the-meter DERs in combination with advanced non-wire alternative (NWA) planning solutions, such as utility-owned storage and distributed PV. Working with a set of least-cost solutions per feeder/substation allows us to benchmark and compare techno-economic performance of different traditional and NWA planning solutions. An important aspect of the LODGE model is that it includes power flow operation constraints. This means that the least-cost planning solutions delivered by LODGE are technically feasible. Available projects in LODGE portfolio include: 1) feeder reconductoring; 2) transformers upgrades; 3) non-wire alternatives, such as strategic siting and sizing of storage and distributed PV; 4) voltage regulators.

Heleno, Miguel↗
DOE OSTI2022

Technical Performance and Cost Optimization of Unobtrusive Multi-static Serial LiDAR Imager (UMSLI) for Wide-area Surveillance and Identification of Marine Life at Marine Energy Installations

Florida Atlantic University developed an underwater optical monitoring system prototype - Unobtrusive Multi-static Serial LiDAR Imager (UMSLI) - suitable for marine energy full project lifecycle observation (baseline, commissioning, and decommissioning), with an automated real-time classification of marine animals. With precursor 2014 DOE funding (award DE-EE0006787), a prototype UMSLI was demonstrated in a controlled laboratory environment and achieved a TRL 6. This EERE DE-EE0007828 award aimed to both increase the TRL of the UMSLI by improving the technology performance (e.g., increase distance of marine animal target detection capability, add additional species classification capabilities, improve the system performance during the day, etc.) and reduce the cost. The UMSLI presents a novel application of underwater distributed Light Detection And Ranging (LiDAR), an advanced remote sensing method that uses light in the form of laser pulses for an application, this paired with an algorithm provides 360 degrees underwater detection, imaging, and classification of marine life. This solution for underwater monitoring of biota preserves the advantages of traditional optical and acoustic solutions while overcoming many associated disadvantages for marine energy site environmental monitoring, such as difficulties in species detection and classification in low light or night and turbid environments. This new approach is a purposefully designed, reconfigurable adaptation of an existing class 3B laser technology into one UMSLI instrument that can be easily mounted on or around different classes of marine energy equipment, such as devices to capture ocean current energy or wave energy. The system uses relatively low average power, utilizes far-red (> 635nm) laser illumination to be invisible and eye-safe to marine animals, is compact, and cost-effective. The equipment is designed for long-term, maintenance-free operations (i.e., current design is targeting more than 7 days continuous operation), to inherently generate a sparse primary dataset that only includes detected anomalies, and to allow robust real-time automated animal classification and identification with a low data bandwidth requirement. The technology’s overarching goal for application, is a system that can be deployed to collect pre-installation baseline species observations at a proposed marine energy deployment site with minimal post-processing overhead. The envisioned system will also produce high-resolution imagery of marine animals through a wide range of conditions and support automated tracking and notification of the presence of managed animals within established perimeters of marine energy equipment to satisfy deployed marine energy projects’ endangered and threatened species monitoring requirements. Through the current project, we demonstrated the UMSLI prototype in an operational environment and increased the UMSLI’s Technology Readiness Level from 6 to 7. The project resulted in many novel technologies, including: 1) an eye-safe red laser based, low-cost LiDAR system that can detect targets up to 10 meters distance; 2) the GAN-based machine learning underwater LiDAR image enhancement technique (this is the first known application of GAN technique in underwater LiDAR); 3) LiDAR-based real-time automated detection capabilities; and 4) a template matching based automated classification tool. These technologies build a solid foundation for future efforts to develop an extended range electro-optical monitoring system suitable for marine energy deployments. In addition to technology development, a key lesson learned is that addressing regulatory and safety requirements must be front and center in any marine energy monitoring applications.

16 TIDAL AND WAVE POWER↗
DOE OSTI2023

Cost-optimized Automated Variance Reduction for Highly Angle-dependent Radiation Transport Analyses [Dissertation]

Monte Carlo variance-reduction techniques that directly bias particle direction have historically received limited attention despite being useful for many radiation transport applications such as those in which radiation travels through large regions with a low probability of colliding. One such technique is known as DXTRAN (short for deterministic transport) in the MCNP Monte Carlo radiation transport code. Until now, effectively applying DXTRAN in calculations is largely based on empirical observations of computational performance. Optimal DXTRAN parameters are identified through manual iteration. This work develops new mathematical descriptions of the DXTRAN variance-reduction process and demonstrates a new automated variance-reduction method that applies these mathematical formulations to determine the optimal application of DXTRAN in a given problem.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Cost‐Responsive Optimization of Nickel Nanoparticle Synthesis

Abstract Early‐stage cost evaluation during catalyst development holds the potential to accelerate the commercialization and deployment of advanced catalytic materials for sustainable chemical processes. The modeling and assessment of manufacturing costs as early as the laboratory synthesis scale, for example, focusing on materials costs and synthesis performance metrics, can support the development of an experimental–economic feedback loop that enables rapid insight into cost drivers associated with catalyst synthesis and highlights areas that require focused research and development effort. Ultimately, this feedback loop supports the realization of an economic understanding of the overall synthetic process and highlights opportunities to reduce costs, serving as the foundation for the scale‐up of catalyst manufacturing. Herein, a case study is presented utilizing CatCost, a free and publicly available estimation tool for the evaluation of catalyst manufacturing costs, to perform a cost‐responsive optimization of the synthesis of nickel nanoparticles (Ni NPs). It is demonstrated that reagent substitutions with more cost‐effective analogs, coupled with stoichiometric optimization, afford a 58% reduction in raw materials cost without changing the product yield or properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Nonlinear programming optimization of a single-stack electrodialysis desalination system for cost efficiency

Electrodialysis (ED) presents a competitive method for desalinating brackish waters. In this work, we perform cost optimization of a single-stack ED system across a range of feed salinities and water recoveries while optimizing operating voltage, number of cell pairs, and cell length. The results of our optimization show that the levelized cost of water (LCOW) increases with an increase in feed salinity. The outcomes of our optimization show that cost-optimal design generally increases cell length while decreasing cell pair number and operating voltage with an increase in salinity. These trends are nonlinear, with the number of cell pairs and applied voltage exhibiting local maxima when operating at low salinity and high recovery. We discuss the underlying mechanism for cell length becoming a leveraging design parameter by inspecting the length-dependent profiles of key electrochemical properties of the ED cell. Finally, we present how increasing performance metrics and decreasing costs impact LCOW, demonstrating that innovations that decrease counter-current diffusion have resulted in the highest decrease of LCOW.

42 ENGINEERING↗
DOE OSTI2022

Commercial Building Sensors and Controls Systems - Barriers, Drivers, and Costs

Optimized building sensors and controls systems could lead to 29% annual energy savings in commercial buildings and are integral to the growth of grid-interactive efficient buildings. However, only 8% of small commercial buildings have installed sensors and controls systems-which is largely due to cost barriers. This publication seeks to increase the transparency of system costs and identify specific barriers and drivers for increased adoption. Qualitative interview data was collected from 20 interviews with industry and qualitative cost data was collected from invoices during the interviews. The greater understanding of costs and barriers associated with commercial building sensors and controls systems lays the groundwork for future steps in increasing system adoption, reducing energy consumption, and market transformation.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗