SEARCH · Engineering Papers

Results for “building operations”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,153 records · Page 64

All sources NASA NTRS DOE OSTI

DOE OSTI2026

Intercomparison of flood inundation models across land use types and hydrological flood stages

Flood Inundation Mapping (FIM) model selection is a key operational decision because accurate, rapid mapping underpins early warning and resource allocation. FIM performance is context-dependent and can vary with hydrograph phase, land-use/land-cover (LULC), and the evaluation benchmark. Intercomparison studies typically assess a single near-peak snapshot against one reference dataset. Here, we provide a context-stratified intercomparison across (i) multiple hydrograph phases, (ii) LULC classes, and (iii) benchmark types, for five FIM approaches spanning a wide range of physical complexity and operational cost (TRITON, LISFLOOD-FP, HEC-RAS 2D, ARC-Curve2Flood, and OWP HAND-FIM). We use the Hurricane Matthew flood (2016) in the Neuse River Basin, North Carolina, USA, as a case study. Using high-resolution remote sensing-derived flood inundation maps, hand-labeled points, and building footprints, we assess model skill across two rising and two falling hydrograph limbs and across major LULC types. Results show that model rankings shift systematically across contexts: LISFLOOD-FP ranks highest in three of four flood phases, while TRITON leads during one rising limb phase; LISFLOOD-FP performs best in vegetated areas, whereas HEC-RAS improves relative performance in agricultural and urban areas; and benchmark choice influences conclusions, with LISFLOOD-FP performing best for flooded-building detection in the late falling limb, while TRITON ranks highest against hand-labeled points. We also report representative wall-clock runtimes for each workflow to provide use-case context for operational feasibility. Together, these results offer transferable guidance for model selection and for designing large-scale, benchmark-aware FIM intercomparison studies.

Nikrou, Parvaneh [University of Alabama]↗

DOE OSTI2026

Artificial Intelligence Thermostat to Detect Faults

Residential air conditioners and heat pumps often experience faults due to inadequate maintenance, which can severely reduce efficiency or even cause system failure. Common issues include dirty or clogged air filters and refrigerant leaks. These problems degrade performance and increase energy use and operating costs. This study presents a smart thermostat with embedded artificial intelligence to detect such faults and alert homeowners when maintenance is needed. The thermostat uses low-cost measurements—including return-air temperature, relative humidity, supply-air temperature, outdoor-air temperature, and condenser subcooling—to identify abnormal operations. Because different faults produce distinct response patterns, tailored algorithms are developed to recognize characteristic fault signatures. The investigation is built on a detailed co-simulation platform that couples EnergyPlus with the DOE/ORNL Heat Pump Design Model (HPDM). EnergyPlus represents the building’s dynamic environment, while HPDM is a high-fidelity, hardware-based model that can simulate fault-free performance as well as a wide range of faults, including gradual degradation such as minor refrigerant leakage. This platform provides a virtual training and testing environment that helps distinguish fault-induced behavior from normal operation and supports development of robust diagnostic algorithms. Using this framework, a Dynamic Bayesian Network was developed to identify two common faults—gradual refrigerant charge loss and indoor airflow blockage—and the AI-embedded thermostat was verified through annual building simulations.

Shen, Bo [ORNL] (ORCID:0000000336600393)↗

DOE OSTI2020

Accelerating Large-Format Metal Additive Manufacturing: How Controls R&D is Driving Speed, Scale, and Efficiency

This article highlights work at Oak Ridge National Laboratory’s Manufacturing Demonstration Facility to develop closed-loop, feedback control for laser-wire based Directed Energy Deposition, a form of metal Big Area Additive Manufacturing (m-BAAM), a process being developed in partnership with GKN Aerospace specifically for the production of Ti-6Al-4V pre-forms for aerospace components. A large-scale structural demonstrator component is presented as a case-study in which not just control, but the entire 3D printing workflow for m-BAAM is discussed in detail, including design principles for large-format metal AM, toolpath generation, parameter development, process control, and system operation, as well as post-print net-shape geometric analysis and finish machining. In terms of control, a multi-sensor approach has been utilized to measure both layer height and melt pool size, and multiple modes of closed-loop control have been developed to manipulate process parameters (laser power, print speed, deposition rate) to control these variables. Layer height control and melt pool size control have yielded excellent local (intralayer) and global (component-level) geometry control, and the impact of melt pool size control in particular on thermal gradients and material properties is the subject of continuing research. Further, these modes of control have allowed the process to advance to higher deposition rates (exceeding 7.5 lb/hr), larger parts (1-meter scale), shorter build times, and higher overall efficiency. The control modes are examined individually, highlighting their development, demonstration, and lessons learned, and it is shown how they operate concurrently to enable the printing of a large-scale, near net shape Ti-6Al-4V component.

Gibson, Brian↗

DOE OSTI2020

Residential Battery Modeling for Control-Oriented Techno-Economic Studies: Preprint

Electrochemical batteries, which serve as electric energy storage devices, are becoming increasingly popular among residential buildings that incorporate solar photovoltaic (PV) systems to help meet their energy needs. Battery economics are affected by performance degradation over time, and managing this degradation can help extend the battery's lifespan. The tradeoff between operational costs/benefits and managing battery degradation is of significant research interest. One of the key factors for assessing battery degradation is the dispatch strategy used to control the charging and discharging of the battery. Conventional dispatch strategies typically use simple rule-based methods, and these overly aggressive charging/discharging cycles can significantly reduce a battery’s life span. Our research seeks to develop optimized dispatch strategies for grid-connected PV homes with a goal of extending battery life while simultaneously taking into consideration utility costs and occupant comfort. To achieve this goal, we adapted lithium-ion battery life- and cyclic-degradation models for use in high-fidelity building simulations, so whole-building and grid-interactive controllers can dispatch the batteries along with other flexible loads. With the help of a co-simulation platform, we performed a simulation study to compute the optimized dispatch strategies for relevant operating conditions brought about by changing geographical locations, weather conditions, and utility pricing. Comparing the optimized strategies with the conventional strategies resulted in a >50% decrease in capacity degradation and >10% average reduction in operational costs during the months of January and July in Fort Collins, Colorado; Phoenix, Arizona; and Portland, Oregon.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2023

The Hidden Flexibility of the Natural Gas Network for Electric Power Operations: A Case Study of a Near-Miss Winter Event

The U.S. power sector has become increasingly reliant on gas pipeline networks to deliver fuel to natural gas power plants. In addition to supplying relatively low-cost fuel, gas networks offer generators flexibility in their operations through the ability to deliver fuel when needed by using gas storage facilities or linepack if the gas network is at an operating point below its design capacity. However, disruptions or stress events on the gas network - like those occurring in the Northeast and Texas in recent years - can result in limitations on gas availability to generators at times when generation is in short supply. Here we examine a period of stress that occurred in the winter of 2022 in the Western United States. Using data on the region's natural gas pipeline network and electric generators, we build an integrated gas and electric model that closely replicates the actual dispatch of the period. We then evaluate the implications of removing flexibility employed by the gas network operator, which during that period curtailed scheduled gas deliveries to other parties to increase deliveries to natural gas power plants, which requested more gas than initially forecasted. We find that without the flexibility supplied by the gas network operator, there would have been curtailment of gas generation due to gas offtake constraints, requiring the power system operator to redispatch relying on more expensive generation or to potentially shed load. A sensitivity exploring a wind drought further exacerbates the strain, illustrating the potential challenge of managing gas and grid interactions as systems move to higher shares of variable renewable electricity. Based on this example, we discuss potential coordination strategies between the two system operators to ensure that the power system can successfully utilize and rely on the flexibility offered by natural gas networks.

03 NATURAL GAS↗

DOE OSTI2023

PANDEMIC: Occupancy driven predictive ventilation control to minimize energy consumption and infection risk

During the SARS-CoV-2 (COVID-19) pandemic, governments around the world have formulated policies requiring ventilation systems to operate at a higher outdoor fresh air flow rate for a sufficient time, which has led to a sharp increase in building energy consumption. Therefore, it is necessary to identify an energy-efficient ventilation strategy to reduce the risk of infection. In this study, we developed an occupant-number-based model predictive control (OBMPC) algorithm for building ventilation systems. First, we collected the occupancy and Heating, ventilation, and air conditioning system (HVAC) data from March to July 2021. Then, four different models (Auto regression moving average-based multilayer perceptron (ARMA_MLP), Recurrent neural networks (RNN), Long short-term memory networks (LSTM), and Nonhomogeneous Markov with change points detection (NH_Markov)) were used to predict the number of room occupants from 15 min to 24 h ahead with an interval output. We found that each model could predict the number of occupants with 85% accuracy using a one-person offset. Furthermore, the accuracy of 15 min of the ahead prediction could reach 95% with a one-person offset, but none of them could track abrupt changes. The occupancy prediction results were used to calculate the ventilation demand using the Wells-Riley equation, and the upper bound can maintain an infection risk lower than 2% for 93% of the day. This OBMPC model could reduce the coil load by 52.44% and shift the peak load by 3 h up to 5 kW compared with 24 × 7 h full outdoor air (OA) system when people wear masks in the space. The occupancy prediction uncertainty could cause a 9% to 26% difference in demand ventilation, a 0.3°C to 2.4°C difference in zone temperature, a 28.5% to 44.5% difference in outdoor airflow rate, and a 10.7% to 28.2% difference in coil load.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

High-performance windows improve thermal survivability of occupants during cold snaps

Exposure to low indoor air temperature is a major contributor to temperature-related mortality during extreme cold events, especially when power outages disrupt operation of space heating systems. This study explores the impact of high-performance windows on the thermal resilience of residential buildings during extreme cold weather and grid power outages, as well as their long-term benefits through energy efficiency and reduced risk of property damage. Building performance simulations were conducted for reference residential buildings in three construction vintages and two major U.S. cities located in cold climate zones, considering two types of extreme cold events: short and severe, and long and milder. Our research found that even houses compliant with current energy codes struggle to maintain safe indoor temperatures for more than a few hours during power outages, necessitating rapid evacuations. High-performance windows can extend the thermal survivability time by up to 3.8 days within a 7-day cold snap and significantly reduce risk of bursting frozen water pipes, depending on the building’s insulation and infiltration level, cold event severity, and occupant vulnerability. This extended thermal safety time is crucial in scenarios where reduced mobility complicates emergency responses in senior housing. In addition to boosting thermal resilience, upgrading older homes with high-performance windows can reduce heating energy consumption by over 18% and cooling energy by 15%. Our findings highlight the need to incorporate thermal resilience assessments into new designs or major retrofits, including the use of typical and extreme weather scenarios and advanced technologies like high-performance windows.

Krelling, Amanda F↗

DOE OSTI2021

Optimizing Process Conditions during Catalytic Fast Pyrolysis of Pine with Pt/TiO 2 —Improving the Viability of a Multiple-Fixed-Bed Configuration

Catalytic fast pyrolysis (CFP) has been identified as a promising pathway for the production of renewable fuels and co-products. However, continued technology development is needed to increase process efficiency and reduce process costs. This report builds upon previous research in which a bifunctional metal–acid Pt/TiO 2 catalyst was utilized in a fixed-bed reactor operated with co-fed H 2 to improve product yield and reduce coke generation compared to conventional CFP methods. Here, we report further process optimization, in which we achieved similar CFP oil carbon efficiency (>35%) and CFP oil oxygen content (<20 wt %) to our previous report while reducing catalyst and equipment costs by increasing time-on-stream between regenerations by 40–95% and decreasing required regeneration time by more than a factor of 2. These process improvements were achieved by conducting parameter sweeps to determine optimum conditions for CFP and regeneration with key variables including pyrolysis temperature, catalytic upgrading temperature, hydrogen partial pressure, and regeneration oxygen concentration. Coupled with comprehensive oil analyses, these data provide foundational insight into the deoxygenation and coking chemistries for CFP under realistic process conditions while also advancing the technology through applied engineering.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Optimizing stochastic algorithms for hadron correlation function computations in lattice QCD using a localized distillation basis

Distillation is a quark-smearing method for the construction of a broad class of hadron operators useful in lattice QCD computations and defined via a projection operator into a vector space of smooth gauge-covariant fields. A new orthonormal basis for this space is constructed which builds in locality. This basis is useful for the construction of stochastic methods to estimate the correlation functions computed in Monte Carlo calculations relevant for hadronic physics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

Mitigation of birefringence in cavity-based quantum networks using frequency-encoded photons

Atom-cavity systems offer unique advantages for building large-scale distributed quantum computers by providing strong atom-photon coupling while allowing for high-fidelity local operations of atomic qubits. However, in prevalent schemes where the photonic state is encoded in polarization, cavity birefringence introduces an energy splitting of the cavity eigenmodes and alters the polarization states, thus limiting the fidelity of remote entanglement generation. To address this challenge, we propose a scheme that encodes the photonic qubit in the frequency degree-of-freedom. The scheme relies on resonant coupling of multiple transverse cavity modes to different atomic transitions that are well-separated in frequency. We numerically investigate the temporal properties of the photonic wavepacket, two-photon interference visibility, and atom-atom entanglement fidelity under various cavity polarization-mode splittings and find that our scheme is less affected by cavity birefringence. Finally, we propose practical implementations in two trapped ion systems, using the fine structure splitting in the metastable D state of 40 Ca + , and the hyperfine splitting in the ground state of 225 Ra + . Furthermore, our study presents an alternative approach for cavity-based quantum networks that is less sensitive to birefringent effects, and is applicable to a variety of atomic and solid-state emitter-cavity interfaces.

Cavity quantum electrodynamics↗

DOE OSTI2022

Improved Particle Swarm Algorithm Using Rubik’s Cube Topology for Bilevel Building Energy Transaction

Following the rapid growth of distributed energy resources (e.g., renewables, battery), localized peer-to-peer energy transactions are receiving more attention for multiple benefits, such as reducing power loss and stabilizing the main power grid. To promote distributed renewables locally, the local trading price is usually set to be within the external energy purchasing and selling price range. Consequently, building prosumers are motivated to trade energy through a local transaction center. This local energy transaction is modeled in bilevel optimization game. A selfish upper level agent is assumed with the privilege to set the internal energy transaction price with an objective of maximizing its arbitrage profit. Meanwhile, the building prosumers at the lower level will response to this transaction price and make decisions on electricity transaction amount. Therefore, this non-cooperative leader-follower trading game is seeking for equilibrium solutions on the energy transaction amount and prices. Additionally, a uniform local transaction price structure (purchase price equals selling price) is considered here. Aiming at reducing the computational burden from classical Karush–Kuhn–Tucker (KKT) transformation and protecting the private information of each stakeholder (e.g., building), swarm intelligence-based solution approach is employed for upper level agent to generate trading price and coordinate the transactive operations. On one hand, to decrease the chance of premature convergence in global-best topology, Rubik’s Cube topology is proposed in this study based on further improvement of a two-dimensional square lattice model (i.e., one local-best topology-Von Neumann topology). Rotating operation of the cube is introduced to dynamically changing the neighborhood and enhancing information flow at the later searching state. Several groups of experiments are designed to evaluate the performance of proposed Rubik’s Cube topology-based particle swarm algorithm. The results have validated the effectiveness of proposed topology and operators comparing with global-best version PSO and Von Neumann topology-based PSO and its scalability on larger scale applications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

Data Center Waste Heat as an Emerging Urban Thermal Hazard: First Field Measurements of Neighborhood-Scale Air Temperature Impacts

Data centers are among the fastest-growing sources of concentrated anthropogenic heat in urban environments. Despite heat flux densities that exceed peak solar irradiance by a factor of 2–6, their thermal impacts on adjacent communities have never been directly measured or reported in the peer-reviewed literature. This short communication addresses that gap by presenting the first vehicle-based traverse measurements of air temperature in residential neighborhoods downwind of operational data centers. Five traverses at four facilities in the Phoenix, Arizona metropolitan area, ranging from a 36 MW single-building data center in Mesa to a 169 MW colocation campus in Chandler, reveal downwind air temperature warming as high as 2.2 °C, with average downwind air temperatures 0.7–0.9 °C warmer than corresponding upwind areas. Thermal signatures were detectable at distances up to 500 m from facility perimeters. The 36 MW Mesa facility rejects waste heat equivalent to the electricity consumption of approximately 40,000 households, while the 169 MW Chandler campus is equivalent to over 180,000 households, both concentrated into footprints smaller than a single residential subdivision. With U.S. data center capacity projected to more than double by 2030, these findings establish data center anthropogenic waste heat as a previously undocumented urban thermal hazard demanding attention from the data center and urban planning communities.

Phoenix↗

DOE OSTI2024

DetermineSystem

SAND2025-04092O DetermineSystem, an open-source utility library, determines the system name from a given supported_systems.ini file, the machine’s hostname, and the “give build” name. The library is part of the GenConfig family of modules. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Braun, Joshua↗

DOE OSTI2023

Expansion of Hazards and Probabilistic Risk Assessments of a Light-Water Reactor Coupled with Electrolysis Hydrogen Production Plants

This report builds upon the body of work sponsored by the Department of Energy (DOE) Light-Water Reactor Sustainability (LWRS) Flexible Power Operation and Generation (FPOG) program that presented generic probabilistic risk assessments (PRAs) for the addition of a heat extraction system (HES) to light-water reactors to support the co-location of a high temperature hydrogen electrolysis facility (HTEF). Probabilistic and deterministic hazards assessments and risk analyses are leveraged throughout this report. Several improvements and new analyses are included in this report. First, higher amounts of detail in the specifications of the generic HTEFs are used to produce scaled results for a 100, 500, and 1000 MW nominal hydrogen production facility. An additional hazard assessment of 1000 kg of hydrogen storage is performed. The facility hazards and footprint are assessed to determine the safe distance required for placement near the nuclear power plant (NPP). Second, specific designs for corresponding HESs for the different levels of support required by the HTEFs are analyzed in the PRA model. Third, a hazards analysis of the specified HTEFs leads not only to effects of the quantified risk assessment for the NPP, but also qualitative hazards assessment for the community. Finally, a seismic analysis and a high winds analysis have each been added to the PRA. The results investigate the applicability of the potential licensing approaches which do not require a full United States (U.S.) Nuclear Regulatory Commission (NRC) licensing review. The PRAs are generic and include listed assumptions. The HTEF design built for this project has further eliminated many conservative assumptions from the prior PRAs in this series. The PRA results indicate that the 10 CFR 50.59 licensing approach is justified due to the minimal increase in initiating event frequencies for all design basis accidents, with none exceeding 7.7%. The PRA results for core damage frequency and large early release frequency support the use of NRC Regulation Guide 1.174 as further risk information that supports a change without a full licensing amendment review. The hazard analyses and PRA confirm the need for engineered blast barriers of storage tanks and the common production header leaving the HTEF. The hazards analyses and PRA also confirm with high confidence that using the assumptions of design in this report that the safety case for licensing an HES addition and an HTEF sited with its unprotected high-pressure stage components 187 meters from the NPP’s transmission towers (the most fragile structure, system, and component) is strong.

08 HYDROGEN↗

DOE OSTI2023

Coal-based Bricks & Blocks (CBBs): Process Development to Prototype Fabrication Coupled with Techno-Economic Analysis and Market Survey

Coal-based bricks and blocks (CBBs) were formulated using anthracite and thermoplastic binder. Lab scale test composites were formed by hot-press molding and commercial extrusion for test articles and for a full-scale prototype. Formulations and test matrices followed a systematic experimental design to examine the effects of and interactions among three primary independent variables: a) thermoplastic type, b) weight fractions of binder relative to the total anthracite weight, and c) relative proportions of large versus small (milled) anthracite size fractions. Technical performance metrics include compressive strength, modulus, porosity/permeability and density. These DoE studies establish composition-property relations and identify the best combination for any desired property of interest – in particular, strength. Based on CBB composition and fabrication process, validated against technical performance metrics, a techno-economic analysis was constructed which incorporated a process flow model, engineering design (with scaling), and calculated capital plus operating costs. Based on measured property metrics and estimated process economics and a projected price by TEA (techno-economic analysis), building and construction target markets were identified by a market survey assessment. With known cost, price, performance metrics and competitive advantages for CBBs matched with commercial applications/uses, a technical gap analysis was performed to identify further development required for scale-up and commercialization.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2024

Hazards and Probabilistic Risk Assessments of a Light-Water Reactor Coupled with Industrial Facilities

This report provides a roadmap and toolkit for site-specific risk assessments across a broad range of industrial customers co-located with nuclear power plants (NPPs). This report builds upon the body of work sponsored by the Department of Energy (DOE) Light-Water Reactor Sustainability (LWRS) Flexible Plant Operation and Generation Pathway that presented hazards assessment and generic probabilistic risk assessments (PRAs) for the addition of a heat extraction system (HES) to light-water reactors co-located with hydrogen production facilities. The report expands the hazards assessments to include other industrial facilities: an oil refinery, a methanol plant, a synthetic fuel (synfuel) plant, the production of synthetic gas (syngas) as part of the methanol and synfuel plants, and wood pulp and paper mills. All these facilities are specified through industrial process and requirements research performed by national laboratories, universities, and interaction with industry. Many of the processes used in this report are pre-conceptual designs to use for decarbonization of the current technology facilities. A process of failure modes and effects analysis (what can go wrong) and accidentology (what has historically gone wrong) was used to determine the hazards presented to the NPP by the addition of the HES and the industrial customer. Chemical properties of feedstocks and products are summarized as part of the hazards assessment. Example analysis procedures are provided for each of the hazard types identified. These deterministic analyses can be used to assess adherence to licensing criteria. They can also be used to meet other safety goals like protection of the public, workers, or industrial facility equipment. The probabilistic analysis consisted of three sizes of HESs modeled in a PRA to assess the impact on the initiating events (IE) and results of the PRA. The PRA results conclude that the resulting increases in IE frequencies are below the limits required for small changes to existing NPPs under 10 CFR 50.59.

08 HYDROGEN↗

DOE OSTI2025

Improved Charge Sensing on a SiMOS Double Quantum Dot using a Cryogenic Skipper Readout ASIC (Quandarum)

Major outstanding questions in high-energy physics such as the nature of dark matter and the existence of interactions beyond the standard model require new measurement techniques which are extremely sensitive to minute electromagnetic fields. An array of entangled spin qubits is a promising system for building novel detectors due to its combination of sensitivity and controllability. CMOS-based electron spin qubits, which have demonstrated the operational requirements for fault-tolerant quantum computing [1], offer a particular opportunity due to their compatibility with classical electronics, which allows the leveraging of decades of development of low-noise cryogenic detectors for physics. In this work, we combine a SiMOS double-quantum dot device architecture with a state-of-the-art cryoelectronic readout circuit [2-3] aimed to demonstrate improved charge readout using a single-electron transistor (SET). We identify the design characteristics for an SET that facilitate the use of on-chip classical electronics as a low-power, high-bandwidth first amplification stage and explore opportunities for sensor-readout co-design to minimize noise. This is the first of a series of steps to demonstrate high-fidelity readout of a large array of spin qubit with enough sensitivity to probe processes of interest for the investigation of beyond-standard-model physics.

Quinn, Adam [Fermilab]↗

DOE OSTI2026

Africa Battery Energy Storage Systems (BESS) Capacity Building Utility-Scale Storage: BESS Valuation, Tariffs, and Remuneration [Slides]

Utility-scale Battery Energy Storage Systems (BESS) are key to enhancing grid reliability, integrating renewable energy, and providing operational flexibility. Designing effective valuation, remuneration, and tariff frameworks is essential to ensure both system benefits and financial viability for developers. This presentation outlines a structured methodology for evaluating BESS projects, covering policy and legal considerations, cost and revenue analysis, benchmarking, financial sensitivity, and risk assessment, while ensuring alignment with public interest. It also explores valuation of multiple storage services - bulk energy, ancillary services, and infrastructure support - and monetization strategies through capacity payments, energy tariffs, tolling, arbitrage, and non-wires alternative payments. Technical factors, including round-trip efficiency, degradation, and storage duration, are integrated into financial and operational modeling to quantify both system-wide and project-level benefits. Through case studies and simulation-based approaches, this framework provides regulators, utilities, and developers with practical guidance for tariff design, payment structures, and investment decisions, maximizing the economic and societal value of BESS deployment.

25 ENERGY STORAGE↗