Production Cost Model Research Articles

Cost modeling for the GWh-scale production of modern lithium-ion battery cells

Battery production cost models are critical for evaluating the cost competitiveness of different cell geometries, chemistries, and production processes. To address this need, we present a detailed bottom-up approach for calculating the full cost, marginal cost, and levelized cost of various battery production methods. Our approach ensures comparability across research fields and industries, reflecting capital and imputed interest costs. We showcase the model with case studies of a prismatic PHEV2 hardcase cell and a cylindrical 4680 cell in four different chemistries. Our publicly available browser-based modular tool incorporates up-to-date parameters derived from literature and expert interviews. This work enables researchers to quickly assess the production cost implications of new battery production processes and technologies, ultimately advancing the goal of reducing the cost of electrified mobility.

A Multi-Decadal Hourly Coincident Wind and Solar Power Production Dataset for the Contiguous United States

As renewable energy continues its rapid expansion in the Unites States, multi-decadal hourly datasets of electricity production are needed to asses reliability and resource adequacy of power grids. Recent years have seen the release of grid-cell-level simulated meteorological variables, however these are not extended to the power domain, are not developed from a dynamically consistent numerical weather model, and only cover a historical baseline of less than a decade. To fill this gap, this work provides a dataset of 43 years of coincident plant-level wind and solar power production data. The dataset is designed to be aggregated to appropriate scales of interest for bulk system studies such as Balancing Authorities (BAs), states, and nodes of a production cost model. The dataset covers every plant in the contiguous U.S. that is reported in the U.S. Energy Information Administration (EIA) Form 860 as of 2020. When compared with the EIA-923 monthly generation, we find minimal bias (less than 5%). When compared with BA-reported hourly generation, we find low bias in solar (less than 7%), and slight underdispersion in wind. This coincident multi-decadal historical dataset provides a documented and evaluated multi-resource baseline for studies on reliability, resource adequacy, climate change impacts, and characterization of emergent climate threats on renewable resources.

Does BEV always help to reduce CO2 emission? impact of charging strategy

The environmental impact of battery electric vehicles (BEVs), which are an integral part of climate change mitigation measures, depends on charging strategies, such as uncontrolled charging, controlled charging (V1G), and discharging (V2G). However, the impact is unclear because power system operation involves the cost of carbon dioxide (CO2) emissions and flexibility requirements for imbalances caused by renewable energies. To clarify the robustness of the environmental impact of BEVs, we assessed the interactions between CO2 prices and flexibility requirements in Japan in 2030 using a production cost model. The results showed that uncontrolled charging increases CO2 emissions, whereas V1G or V2G reduces them. Further, the impact of BEVs was non-monotonic with respect to CO2 prices, implying that policymakers should carefully evaluate CO2 prices to achieve decarbonization as BEVs do not always reduce CO2 emissions. This study provides useful insights for policymakers in countries with low-cost, inflexible, and high-emission power plants.

Cost modelling for dimension stone quarry operations

Dimension stone mining is Increasing because of growing demand from the building and construction industries. Diamond wire saw cutting is a popular dimension stone mining technique around the world. Estimation of the production cost for this method is critical for these operations. In this research a production cost model was developed for the mining of granite using a diamond wire saw. Cost and production data were collected from a granitic dolerite operation in Pakistan for a 4.5-year period, and analysed to identify the cost components. Fuel (34%), labour (15%), consumables (40%), and maintenance (5%) were found to be the four major cost components in this operation. A regression-based dimension stone production cost model incorporating statistically significant variables was developed. The data presented a strong correlation of production cost with fuel, labour, and consumables costs. Finally, a production cost model based on the consumption cost of diamond wire is presented. The model was verified using actual production cost data, which indicated 95% (R2 = 0.95) variance in the data between the predicted and actual production cost values. The model may help field engineers in computing their dimension stone operational cost during the cutting phase.

An open-source framework for balancing computational speed and fidelity in production cost models

Abstract Studies of bulk power system operations need to incorporate uncertainty and sensitivity analyses, especially around exposure to weather and climate variability and extremes, but this remains a computational modeling challenge. Commercial production cost models (PCMs) have shorter runtimes, but also important limitations (opacity, license restrictions) that do not fully support stochastic simulation. Open-source PCMs represent a potential solution. They allow for multiple, simultaneous runs in high-performance computing environments and offer flexibility in model parameterization. Yet, developers must balance computational speed (i.e. runtime) with model fidelity (i.e. accuracy). In this paper, we present Grid Operations (GO), a framework for instantiating open-source, scale-adaptive PCMs. GO allows users to search across parameter spaces to identify model versions that appropriately balance computational speed and fidelity based on experimental needs and resource limits. Results provide generalizable insights on how to navigate the fidelity and computational speed tradeoff through parameter selection. We show that models with coarser network topologies can accurately mimic market operations, sometimes better than higher-resolution models. It is thus possible to conduct large simulation experiments that characterize operational risks related to climate and weather extremes while maintaining sufficient model accuracy.

Benefits of Multi-Terminal HVdc Under Extreme Conditions via Production Cost Modeling Analyses

Benefits of Multi-Terminal HVdc Under Extreme Conditions via Production Cost Modeling Analyses

Residential demand response program modelling to compliment grid composition and changes in energy efficiency

The path towards decarbonization in Canada is heavily reliant on electrification if the electricity grid is decarbonized. Besides the effectiveness in emission reduction, electrification offer opportunities to increase grid flexibility through demand response (DR) programs. Despite being widely seen in literature that DR programs are beneficial to the grid, there are limited, if any, residential DR programs available. As Canada has a diverse range of electricity grids, this study aims to determine how residential building DR programs impact grid operations on different archetypical electricity grids. The production cost model, SILVER, is used for this analysis. Additionally, various demand profiles are constructed to mimic changes in building stock efficiencies. It is found that grid composition plays a larger role in the effectiveness of a residential DR program than the efficiency of the building stock. Furthermore, increasing the efficiency of the building stock led to DR being more effective on grids where there are significant variable renewable energy sources. These results should motivate cities to consider residential DR programs as a tool to reducing emissions along with benefiting grid operations.

The importance of capturing power system operational details in resource adequacy assessments

Traditional methods for assessing the resource adequacy (RA) of a power system are becoming obsolete due to emerging trends such as the increasing deployment of variable renewable energy and storage. Consequently, analysts are recommending that RA be assessed using a Monte Carlo simulation approach that models chronological power system operations over many instances of possible operating conditions. However, this approach is necessarily more complex and computationally demanding, which is an obstacle to real-world implementation. In this study, we investigate which operational details of power systems are important to capture in order to accurately evaluate a system’s RA, versus details that add complexity but do not meaningfully affect RA results. To do so, we develop a probabilistic RA assessment framework by adapting an existing production cost model and apply it to a case study based on the IEEE Reliability Test System. Our results indicate that multi-year data, storage dispatch, and transmission limits are key details to incorporate. Accurate RA results can be obtained using non-economic dispatch strategies as long as they are coordinated with detailed operational strategies. We also demonstrate how popular expectation-based RA metrics can mask important differences in the characteristics of loss of load events.

Production Cost Modeling and Simulation in the Glove Manufacturing Industry

Product costing is an essential aspect of business strategy as it allows companies to forecast a product's future revenues and expenses and make informed decisions about its development and production. One of the main challenges in the manufacturing sector is the difficulty in selecting the optimal production setup. This can be due to changes in the product or component during the manufacturing process, leading to difficulties in defining the best production quantity and forecasting production costs. Low productivity is another challenge faced by industrial organizations, which can affect their profitability. A case study was conducted in the glove manufacturing industry. The main objective of this research is to model the production cost for the selected case study. Cost modeling in the manufacturing industry involves creating a representation or simulation of a manufacturing process to estimate the costs associated with producing a product. Developing a cost model for the manufacturing industry involves collecting data from the industry and existing literature; developing a cost model with several function modules based on the data; validating the model by comparing its estimates to actual costs; and using the model for cost estimation, budgeting, and product pricing while keeping it updated and calibrated regularly to ensure its accuracy over time. Based on simulation analysis, productivity was improved by 4.0% compared to the original layout. In addition, the production cost per box was reduced by 4.2%. The results from this research can help companies to manage their resources and improve their profitability more effectively.

Opportunities for wave energy in bulk power system operations

Wave energy resources have high, yet largely untapped potential as candidate generation technology. In this paper, we perform a data-driven analysis to characterize the impact of wave energy integration on bulk-scale power systems and market operations. Through data-driven sensitivity studies centered on an optimization-based production cost modeling formulation, our work characterizes the inflection point beyond which wave integration starts impacting power system operations, considering present day transmission infrastructure. Furthermore, our analysis also considers the joint effects of wave energy integration and system-wide transmission expansion. Finally, potential resilience scenarios such as wildfire-driven transmission contingencies and heat wave events are investigated, whereby the contributions of grid-integrated wave energy in alleviating the effects of the resilience events are analyzed. As our demonstration test bed, we consider a reduced-order network topology for the U.S. Western Interconnection with wave energy generation integrated at carefully selected sites across the coastal areas of Washington, Oregon, and northern California. Our results indicate that over a representative year of operations, wave energy integration systematically reduces locational marginal prices (LMPs) of energy and price volatility, especially during periods of high wave resource availability (winter months for the U.S. west coast). Average, maximum, and minimum of hourly LMPs over a typical year of operation was reduced by 2.95, 51.28, and 1.13 $/MWh respectively (over a baseline scenario with no wave energy integration), when the selected network model had a total of 5000 MW wave power installed capacity during the representative year of study. The effects of wave energy integration can remain localized with existing transmission infrastructure (identified to be most pronounced in the Pacific Northwest region in the example we studied). However, with concurrent transmission expansion, the impacts of wave energy integration are likely to have a higher geographical spread. Our results also indicate that wave energy may be able to assist power system operations during resilience events such as major transmission contingencies and heat wave events, although such benefits might be dependent on factors such as proximity of affected area to wave resources, availability of adequate resource potential and adequate transmission capacity.

Quantifying the value of probabilistic forecasting for power system operation planning

A recent key research area in renewable energy integration is the development of tools and methods to capture and accommodate the uncertainty associated with the forecast errors. While the research community has proposed numerous methods to improve the accuracy of probabilistic forecasts, their application to operational planning is still an open question. This work applies dynamic reserve determination methods to solar probabilistic forecasts and then feed them to a commercial production cost model simulator to assess the value of capturing the uncertainty endogenously in the reserve determination process. Testing is carried out on a calibrated real-size system representing the Southern Company for medium, and high solar penetration levels. Numerical results demonstrate the benefits that can be attained by explicitly modeling probabilistic uncertainty in terms of operating cost, and enhanced system reliability which is measured as the quantity of balancing and reserve violations. Additionally, these methods and results can pave the way for system operators to adopt probabilistic forecasting to draw the operating plans of the system, and this allowing the successful integration of variable renewable energy sources.

Production cost modeling for permanent magnet synchronous machines for electric vehicles

A cost model for the estimation of production costs of permanent magnet synchronous machines (PMSM) is presented, which allows to alter design choices such as wire technology, winding layout, cooling system, materials and more. With the goal to make results reproducible by others, the methods are explained in detail and used data and assumptions are given. The developed model helps to understand the interaction between the design of PMSM, manufacturing methods and the resulting costs. With it, different PMSM technologies and materials can be evaluated regarding its influence on the production costs, which is a perquisite to find the best compromise between performance and costs. Production volume is shown to be the most decisive factor for the resulting production costs. Between minimum and maximum assumed volumes, an average cost per unit reduction of 67% could be observed. Furthermore, the results imply that the winding production is responsible for the greatest part of the overall costs, followed by the rotor assembly (including rare earth magnets). When using the model to compare different wire types, it can be stated that up to a production volume of roughly 150,000 units/year, hairpin wires are more expensive to produce. Above this volume, hairpin windings will get cheaper than round wire windings due to its higher grade of automation of the production process. Through the conducted investigations and the presented results, it is demonstrated that the cost model can serve to evaluate technologies with regards to costs in the early development stage. This way a more holistic assessment of technologies for PMSM is possible, helping to find the ideal compromises between costs and performance and to increase the attractiveness of sustainable mobility.

Killer to cure: Expression and production costs calculation of tobacco plant-made cancer-immune checkpoint inhibitors.

Immune checkpoint inhibitors (ICIs) have achieved huge clinical success. However, many still have limited response rates, and are prohibitively costly. There is a need for effective and affordable ICIs, as well as local manufacturing capacity to improve accessibility, especially to low-to-middle income countries (LMICs). Here, we have successfully expressed three key ICIs (anti-PD-1 Nivolumab, anti-NKG2A Monalizumab, and anti-LAG-3 Relatimab) transiently in Nicotiana benthamiana and Nicotiana tabacum plants. The ICIs were expressed with a combination of different Fc regions and glycosylation profiles. They were characterized in terms of protein accumulation levels, target cell binding, binding to human neonatal Fc receptors (hFcRn), human complement component C1q (hC1q) and various Fcγ receptors, as well as protein recovery during purification at 100 mg- and kg-scale. It was found that all ICIs bound to the expected target cells. Furthermore, the recovery during purification, as well as Fcγ receptor binding, can be altered depending on the Fc region used and the glycosylation profiles. This opens the possibility of using these two parameters to fine-tune the ICIs for desired effector functions. A scenario-based production cost model was also generated based on two production scenarios in hypothetical high- and low-income countries. We have shown that the product accumulation and recovery of plant production platforms were as competitive as mammalian cell-based platforms. This highlights the potential of plants to deliver ICIs that are more affordable and accessible to a widespread market, including LMICs.

Multi-timescale operations of nuclear-renewable hybrid energy systems for reserve and thermal product provision

In this paper, an optimal operation strategy of a nuclear-renewable hybrid energy system (N-R HES), in conjunction with a district heating network, is developed within a comprehensive multi-timescale electricity market framework. The grid-connected N-R HES is simulated to explore the capabilities and benefits of N-R HES of providing energy products, different reserve products, and thermal products. An N-R HES optimization and control strategy is formulated to exploit the benefits from the hybrid energy system in terms of both energy and ancillary services. A case study is performed on the customized NREL-118 bus test system with high renewable penetrations, based on a multi-timescale (i.e., three-cycle) production cost model. Both day-ahead and real-time market clearing prices are determined from the market model simulation. The results show that the N-R HES can contribute to the reserve requirements and also meet the thermal load, thereby increasing the economic efficiency of N-R HES (with increased revenue ranging from 1.55% to 35.25% at certain cases) compared to the baseline case where reserve and thermal power exports are not optimized.

Historical wind power in Karnataka differs from predictive models: A granular analysis of performance across climatology, technology, and location

Wind power constitutes one of the largest forms of renewable energy in India. Many operational models have predicted wind energy in India, but most rely on satellite reanalysis data. In contrast, this paper examines historical generation data at an unprecedented granularity to understand its potential and variance. We find actual wind capacity factor (CF) during the Indian Summer Monsoon to be less than half that predicted in NREL’s recent landmark production cost modeling study. Further, we find that the yearly moving average of aggregated CF exhibits a +0.640 correlation with the ENSO SOI, a widely-used climatological parameter. At the feeder level, we confirm an expected exponential relationship between average CF and turbine hub height. However, this effect is 261% more pronounced during the monsoon, a finding with troublesome implications for grid planning. We also test the assumption that higher wind turbines perform proportionately better outside of the monsoon and find no evidence to support it. Finally, we present a new method to measure the geographic smoothing effect that reduces inherent variability by 18% when compared to normalized standard deviation. We find limits to the effect of decreasing variability through geographic diversity; the effect mostly disappears at about 125 km.

Dynamic Security Analysis Framework for Future Large Grids With High Renewable Penetrations

The 100% renewable energy targets from the policymakers for the future grids have drawn a significant amount of interest. The high renewable penetration made future large interconnected grids (LIGs) more volatile and harder to understand using historical observations. This led to the need to study future LIGs using a wide range of future-year operating conditions and contingencies. However, existing planning tools are not sufficient for the dynamic security assessment (DSA) of these future LIGs due to a lack of detailed modeling capabilities and computational limitations when processing a wide range of scenarios. This paper addresses these two challenges by proposing; 1) an efficient modeling framework that can generate large grid’s dynamic data with cascade behaviors for a wide range of scenarios. This data is generated by respecting the constraints of production cost models and their respective AC power flow dynamic simulation models at an hourly resolution; 2) an unsupervised machine learning(ML)-based approach for fast scanning the DSA data. The proposed approach uses feature engineering techniques and fast Fourier transforms to transform the time series signals into visually distinguishable frequency domain signals for DSA. The proposed simulation framework is used to generate 1.485 terabytes of dynamic simulation data for the 2028 WECC system containing 4455 scenarios. The proposed ML framework used the 2028 WECC system to demonstrate its effectiveness and speed in identifying critical scenarios.

From coal to variable renewables: Impact of flexible electric vehicle charging on the future Indian electricity sector

India ranks third globally in overall greenhouse gas (GHG) emissions, with power and transportation first and third, respectively, in terms of India's sectoral GHG contributions. Motivated by India's Nationally Determined Contribution goals and stated intent to drive renewables growth and vehicle electrification, this paper explores the connections between India's power and road transportation sectors by investigating the benefits of flexible electric vehicle (EV) charging on a set of hypothetical 2037 Indian power grid infrastructures. Using two production cost models, one simulating optimal unit commitment and hourly dispatch of available generation, the other simulating subhourly real-time dispatch operations, we examine the impact of flexible and inflexible EV charging regimes and vehicle-to-grid integration on generation profiles, peak load shaving, energy storage utilization, and overall emissions. Our techno-economic analysis shows that a number of future grid infrastructure scenarios, despite not specifically accounting for EV growth scenarios, may meet annual and hourly peak demand when combined with flexible EV charging; in contrast, inflexible EV charging, particularly during the early morning, will lead to substantial need for additional generation capacity. Coupled sectoral decarbonization remains tied to the grid generation mix, highlighting the need for continued policy focus on decarbonization of the rapidly growing power sector.

Hydropower operation in future power grid with various renewable power integration

Hydropower generation may play an increasingly important role in the power grid under increasing contribution of variable renewable sources such as wind and solar. An improved understanding of the changes to hydropower dispatch under future higher VRE grid conditions reveals research gap that should be informed power grid planning and reservoir water releases policies considering multiple other water uses and varying hydrologic condition. This study aims to understand the role of hydropower in a changing power grid by employing a production cost model, PLEXOS, across future power system scenarios, planning horizons, and regions. We explore optimized hydropower dispatch to understand its potential role in minimizing the system cost and renewable curtailment. We also examine the sensitivity of hydropower revenue under various grid scenarios of the Eastern U.S. and hydrology conditions. Results indicate hydropower generation follows net load and compensates for the variability of solar and wind generation. Although energy prices are lower during some periods in the future grid scenarios, there is a potential for higher revenue for hydropower by providing both energy and ancillary services during times of stress. Further, hydropower revenue is sensitive to hydrology in the SERC region, which we considered as an example. The feasibility of hydropower dispatching with higher ramps between low and high hourly-capacity factors, as indicated in the optimization model, requires further study to consider other water use and ecology constraints.

Analysis of multi-output hybrid energy systems interacting with the grid: Application of improved price-taker and price-maker approaches to nuclear-hydrogen systems

The growing recognition of the value of hydrogen as an energy intermediate in supporting future power systems with high shares of variable renewable energy has prompted many studies to quantify the economic potential of multi-output hybrid systems, which are one type of integrated energy systems (IES). Because of the complexity of modeling multiple sectors, these studies typically use simplified modeling approaches to capture the interactions between sectors. In this study, we explore the implications of alternative modeling approaches for nuclear-hydrogen IES focusing on a power system in the Midwest United States. We combine highly resolved capacity expansion and production cost modeling tools of the power system with a detailed hydrogen system optimization tool to determine the optimal electrolyzer and storage sizing and optimal operations of the nuclear-hydrogen hybrid resource across three future study years. We compare economic and operational outcomes across a spectrum of modeling approaches, including a non-hybridized base approach; a traditional price-taker approach that does not include the impact of hydrogen production on the electricity system; a power-system-focused price-maker approach that does not account for temporal hydrogen constraints; and two improved price-taker and price-maker approaches that each address the impact of revenue-optimal levels of electricity production on the resulting power system and temporal hydrogen constraints on the overall feasible solution. Results show how a traditional price-taker approach can overestimate the economic benefits of multi-output nuclear-hydrogen IES compared to our two improved approaches that estimate both hydrogen system constraints and power system interaction. We find that hydrogen output requirements and storage size limits are key drivers to overall operations and some economic outcomes. Under our assumed constant hydrogen output requirement, storage costs, test system, and modeling approaches, our results indicate that hybridization can provide a net benefit, but results are sensitive to the treatment of hydrogen revenues and electricity prices as impacted by the power system evolution.

Fuel cell system production cost modeling and analysis

Fuel cell technology is seen as a promising option for decarbonizing global mobility. The costs of fuel cell systems are currently too high for increasing market penetration. The Chair of Production Engineering of E-Mobility Components at RWTH Aachen University has developed a cost calculation model for Polymer Electrolyte Membrane (PEM) fuel cell systems based on the method of Process Based Cost Modeling (PBCM) and is investigating possibilities for reducing manufacturing costs through economies of scale in production. The aim of this publication is therefore to present the results of cost modeling of PEM fuel cell systems and to derive design areas for technical innovations in terms of cost reduction through economies of scale in production. To this end, technical production parameters with the greatest influence on the cost structure of PEM fuel cell systems are identified and ranked by applying a sensitivity analysis. Finally, this work provides a recommendation for strategic investments and formulates the need for further research to reduce PEM fuel cell system costs by economies of scale.