Production Cost Model Research Articles

Production cost model of the multi-jet-fusion technology

The paper presents a model of the production costs of the multi-jet-fusion technology that is based on a model of production costs of the selective laser sintering technology. The model is developed using the methodology of analysis of the event-driven process chain, which consists of modeling, batch assembly, setup, building, removal, and blasting activities. Production costs of each of the activities are separated to direct (labor, material, and energy) costs and indirect (equipment, overheads, and other indirect) costs. The developed model represents a basis for the development of algorithms and software tools for the calculation of the production costs of the multi-jet-fusion technology, since it defines all the necessary inputs and calculation procedures that enable the calculation of the total costs of a batch of products. Besides, the paper presents a procedure for the estimation of production costs that are attributed to a single product or product type.

Value and economic estimation model for grid-scale energy storage in monopoly power markets

Recent trend in increasing the penetration level of renewable energy challenges safety and stability of the power grid. Electrical energy storage (EES) has been deemed as one of the key elements needed to mitigate the fluctuant and intermittent nature of renewables, and is undergoing rapid development nowadays. However, there are still many questions regarding the benefits and drawbacks that EES technologies will present to society, as well as the economic feasibility of EES in monopoly power markets. Given this, this paper presents a grid-scale production cost model for monopoly power markets in which EES generates profits by offering both energy and ancillary services. The production costs of the power system with and without an EES power plant was compared in order to generate a value estimation. Results indicate that the value of EES is related to the unit fuel price and the start-up cost of thermal power units, the penetration level of wind power and size of the EES system. Based on these variables, a value-driven pricing mechanism for EES was put forward and compared with other policies. Economic viability analysis revealed that the pricing mechanism currently used in monopoly power markets are highly inaccurate in capturing the value generated by EES.

A multi-scale calibration approach for process-oriented aggregated building energy demand models

Long-term energy planning relies largely on projections of future energy demand and hourly load profiles. Aggregate building models are increasingly being utilized to characterize the sensitivity of current and future building stocks to changes in climate, population, and building technology on city-to-regional-to-national scales. Due to challenges in the availability of data, those analyses have been limited to projection of energy demand at a single scale, usually state or country, while long-term planning power system models and production cost models might operate at different spatial scales such as energy regions which are focused on ensuring adequate generation infrastructure. We propose and evaluate a novel method to calibrate an aggregate building energy demand model (PNNL's BEND model) against the best available data at the spatial scale of balancing authorities. This approach extends previous work on aggregated building energy demand by facilitating analysis of building energy demand across scales, in particular policy and operational decision-making scales. We show that the bias-corrected model estimates building electric loads reasonably well compared with estimates from a statistical model, but has the additional feature of flexibility across spatial scales. While the calibration approach is presently U.S.-centric and associated with U.S. energy regions, it can be extrapolated to other worldwide regions with similar scale challenges between policy and operational implementation decision making. We discuss the significant challenges involved in formulating and calibrating a complex physical model based on simulations of roughly 100,000 individual buildings against available aggregate regional electric load data and highlight areas for potential future work and improved data collection.

Intercomparison between Switch 2.0 and GE MAPS models for simulation of high-renewable power systems in Hawaii

BackgroundNew open-source electric-grid planning models have the potential to improve power system planning and bring a wider range of stakeholders into the planning process for next-generation, high-renewable power systems. However, it has not yet been established whether open-source models perform similarly to the more established commercial models for power system analysis. This reduces their credibility and attractiveness to stakeholders, postponing the benefits they could offer. In this paper, we report the first model intercomparison between an open-source power system model and an established commercial production cost model.ResultsWe compare the open-source Switch 2.0 to GE Energy Consulting’s Multi-Area Production Simulation (MAPS) for production-cost modeling, considering hourly operation under 17 scenarios of renewable energy adoption in Hawaii. We find that after configuring Switch with similar inputs to MAPS, the two models agree closely on hourly and annual production from all power sources. Comparing production gave a coefficient of determination of 0.996 across all energy sources and scenarios, indicating that the two models agree on 99.6% of the variation. For individual energy sources, the coefficient of determination was 69–100.ConclusionsAlthough some disagreement remains between the two models, this work indicates that Switch is a viable choice for renewable integration modeling, at least for the small power systems considered here.

A comparison of price-taker and production cost models for determining system value, revenue, and scheduling of concentrating solar power plants

Concentrating solar power (CSP) plants convert solar energy into electricity and can be coupled with low-cost thermal energy storage to provide a dispatchable renewable resource. Maximizing the economic benefits of CSP systems requires deliberate selection of the timing of thermal energy dispatch to coincide with high-price or high-value periods. The performance and value of CSP systems have traditionally been evaluated using two distinct approaches: (1) production cost models (PCMs), which optimize commitment and dispatch schedules for an entire fleet of generators to minimize the cost of satisfying electricity demand; and (2) price-taker (PT) models, which optimize dispatch of the CSP system against historical or forecasted electricity prices to maximize revenue available to the CSP operator. The PCM approach is, in principle, superior because of the ability to capture system-level details such as transmission pathways and associated constraints, and feedback between CSP dispatch schedules and operation of other generators. The computationally simpler PT technique cannot explicitly address these concerns, but the comparative reduction in solution time is attractive for sensitivity analysis and rigorous optimization of CSP plant design and operation. In this work, we directly compare the dispatch profiles, net revenue, and operational value of CSP systems using implementations of each technique to assess how closely the solutions from the PT technique can approach those from the PCM for a specific market scenario. The PT implementation produced characteristically similar dispatch profiles as the PCM for both a large solar-multiple configuration and a small solar-multiple peaking configuration, while requiring only 1% of the computational time of the PCM. In addition, dispatching the CSP plant according to the self-scheduled PT solution—as opposed to the system-optimized PCM solution—only reduced the total CSP operational value by 1–5%, with consistent trends across CSP configurations and locations. This analysis validates the ability of the PT approach to substantially capture the economic benefits of CSP systems when pricing profiles are available, and indicates that the PT approach is suitable for initial design and optimization of CSP technologies.

Temporal Decomposition for Improved Unit Commitment in Power System Production Cost Modeling

Long-term planning in power systems requires simulations of unit commitment (UC) for long time periods up to 20 years. Such simulations are conducted with production cost models (PCMs), which involve solving large-scale mixed-integer programming (MIP) problems with a large number of variables and constraints, because of the long planning horizon. We have developed new optimization modeling and solution techniques based on a decomposition scheme to reduce the solution time and improve the accuracy in PCMs. We propose a temporal decomposition that solves the UC problem by systematically decoupling the long-horizon MIP problem into several subhorizon models. The decomposition is obtained by the Lagrangian relaxation of the time-coupling constraints such as ramping constraints and minimum uptime/downtime constraints. The key challenge is to solve several sub-MIP problems while effectively searching for dual variables to accelerate the convergence of the algorithm. We implement the temporal decomposition in an open-source parallel decomposition framework, which can solve the multiple subproblems in parallel on high-performance computing clusters. We also implement the branch-and-bound method on top of the decomposition in order to find a primal optimal solution. Numerical results of the decomposition method are reported for the IEEE 118-bus and PEGASE 1354-bus test systems with up to an 168-hour time horizon.

Reduced-form models for power market risk analysis

The experience of the last fifteen years has illustrated dramatically the emergence of new risks facing power market investors. The volatility of commodity prices, the strategic behavior of competing firms, and regulatory uncertainty all contribute to a challenging investment and operating environment. Traditionally, utilities and power-market investors have used large-scale optimizing production-cost models to analyze the cash flows of power generators. The complexity of these models, particularly when applied on a regional or national scale, is such that computational costs often prohibit extensive analysis of commodity, regulatory, and structural risks. This article demonstrates how a reduced-form modeling approach utilizing neural networks can be used to increase greatly the ability of modelers to use modern simulation-based risk analysis techniques. In particular, several applications relevant to evaluating the cash flow risks of generators, with applications to hedging, are presented. Central to the contributions of this paper is our reduction of complex optimizing models to spreadsheet form, reducing not only their computational complexity, but also their practical user complexity.

Opportunities for Joint Water–Energy Management: Sensitivity of the 2010 Western U.S. Electricity Grid Operations to Climate Oscillations

Abstract The increasing interconnectedness of energy and water systems makes it important to understand how interannual variations in water availability—and climate oscillations—could potentially impact the electric grid operations. The authors assess the vulnerability of the current western U.S. grid to historical climate variability using multiple energy and water system models. A 55-yr-long natural water availability benchmark is combined with the 2010 level of water demand from an integrated assessment model to drive a large-scale water management model over the western United States. The regulated flow at hydropower and thermoelectric power plants is then translated into boundary conditions for electricity generation in a production cost model. This analysis focuses on assessing regional interdependencies and the impact of interannual changes in water availability on power system operations, including reliability, cost, and carbon emissions. Results for August grid operations—when stress on the grid is often highest—show a range of sensitivity in production cost (–8% to +11%) and carbon emissions (–7% to +11%), as well as a 1-in-10 chance that electricity demand will exceed estimated supply. The authors also show that operating costs are lower under neutral El Niño–Southern Oscillation (ENSO) conditions than under other ENSO phases; carbon emissions are highest under La Niña conditions, especially in California; and the risk of brownouts may be higher under neutral and negative ENSO conditions. These results help characterize the grid’s performance under historical climate variations, are useful for seasonal and multiyear planning of joint water–electricity management, and can be used to support impact, adaptation, and vulnerability analyses.

Application-Oriented Robust Design Optimization Method for Batch Production of Permanent-Magnet Motors

From the perspective of industrial production, the design and optimization of electrical machines are application oriented, including maximizing production quality and minimizing production cost in terms of different manufacturing conditions. To achieve these goals, this study presents an efficient application-oriented robust design optimization method for permanent-magnet (PM) motors. The method consists of two main contributions. The first one is the development of an overall optimization strategy, including qualitative and quantitative analyses to provide possible options for an application. Multiphysics analysis, uncertainty analysis, production cost, and optimization models need to be investigated. The second one proposes a multilevel optimization method for the high-dimensional robust design problem of each option. To illustrate the advantages of the proposed method, PM motors with soft magnetic composite cores are investigated for domestic applications. The design optimization is conducted in terms of three motor options and three batch production volumes for both conventional deterministic and robust approaches, and it consists of 18 high-dimensional multiphysics optimization problems in total. Main optimization results are presented and discussed. Experimental and simulation results are presented to validate the effectiveness of the proposed models and methods.

How low can you go? The importance of quantifying minimum generation levels for renewable integration

One of the significant limitations of solar and wind deployment is declining value caused by the limited correlation of renewable energy supply and electricity demand as well as limited flexibility of the power system. Limited flexibility can result from thermal and hydro plants that cannot turn off or reduce output due to technical or economic limits. These limits include the operating range of conventional thermal power plants, the need for process heat from combined heat and power plants, and restrictions on hydro unit operation. To appropriately analyze regional and national energy policies related to renewable deployment, these limits must be accurately captured in grid planning models. In this work, we summarize data sources and methods for U.S. power plants that can be used to capture minimum generation levels in grid planning tools, such as production cost models. We also provide case studies for two locations in the U.S. (California and Texas) that demonstrate the sensitivity of variable generation (VG) curtailment to grid flexibility assumptions which shows the importance of analyzing (and documenting) minimum generation levels in studies of increased VG penetration.

Deploying storage assets to facilitate variable renewable energy integration: The impacts of grid flexibility, renewable penetration, and market structure

As variable renewable wind and solar resources constitute a larger fraction of electricity supply, interest in flexible resources, including demand response, dispatchable generation, transmission interconnection, and storage technologies is growing. This paper evaluates the utility of storage assets given different electricity system configurations, market paradigms, and management schemes using a production cost model. When a large share of non-renewable generation is derived from inflexible sources, storage is imperative even at relatively low renewable penetrations. At high renewable penetrations, storage becomes increasingly important in flexible systems as well. In addition to renewable penetration, storage utilization rates are sensitive to the storage assets' market bidding structure and ownership paradigm, particularly in inflexible electricity systems. Storage assets consume during low-cost hours and generate during high-cost hours, regardless of bidding structure, but the price delta is largest for a centrally-controlled storage asset bidding with a price arbitrage motive. Storage utility cannot be deduced from an individual metric, but rather depends on the interaction between bidding mechanism, renewable penetration, and system flexibility.

Exponentially Accurate Temporal Decomposition for Long-Horizon Linear-Quadratic Dynamic Optimization

In this work, we investigate a temporal decomposition approach to long-horizon dynamic optimization problems. The problems are discrete-time, linear, time-dependent and with \rev{box constraints} on the control variables. We prove that an overlapping domains temporal decomposition, while inexact, approaches the solution of the long-horizon dynamic optimization problem exponentially fast in the size of the overlap. The resulting subproblems share no solution information and thus can be computed independently in parallel. Our findings are demonstrated with a small, synthetic production cost model with real demand data.

The benefits of grid-scale storage on Oahu

The Hawaiian Electric Company intends to procure grid-scale Battery Energy Storage System (“BESS”) capacity. The purpose of this study is to determine whether providing contingency reserve or time-of-day shifting is of more benefit to the Oahu grid, and to better understand the relationship between BESS size and level of benefit. This is an independent study by Sandia, and is not being used to support the regulatory case for BESS capacity by Hawaiian Electric. The study team created a production cost model of the Oahu grid using data primarily from the Hawaiian Electric Company. The proposed BESS supplied contingency reserve in one set of runs and time-of-day shifting in another. Supplying contingency reserve led to larger savings than time-of-day energy shifting. Assuming a renewable reserve and a quick-start reserve, and $15/MMBtu for Low-Sulphur Fuel Oil, the 50-MW/25-MWh, 100-MW/50-MWh, and 150-MW/75-MWh systems supplying contingency reserve provided, respectively, savings of 9.6, 15.6, and 18.3 million USD over system year 2018. Over the range of fuel prices tested, these cost savings were found to be directly proportional to the cost of fuel. As the focus is the operational benefit of BESS capacity, the capacity value of the BESS was not included in benefit calculations.

Custos de produção na empresa e no mercado: uma visão

Um dos dilemas fundamentais da firma reside em executar determinada atividade no mercado, ou no seu interior. Na literatura é a economia dos custos de transação (ECT) que melhor permite esta análise, apresentando a forma de governança adequada para determinada atividade. Contudo, a ECT sozinha não permite analisar completamente esta questão, por não abordar em profundidade o papel da configuração dos recursos produtivos na eficiência de determinada atividade. Duas questões a este respeito ainda são prementes: Quais os fundamentos da eficiência do mercado e das firmas que definem o custo da atividade nestes dois meios? Qual é a estrutura comparativa dos custos relativa à execução de determinada atividade nestes dois meios? Diante destas duas questões este artigo tem por objetivo propor um modelo dos custos de produção na empresa e no mercado, baseado na eficiência dos fatores de produção e nos custos de transação. Esta proposta é feita à luz dos pressupostos da ECT, da organização industrial (OI) e da visão baseada nos recursos (VBR). Como principal resultado este modelo permite compreender como os custos de transação e a eficiência dos fatores de produção determinam o desenvolvimento das empresas e do sistema econômico como um todo.

Emissions implications of downscaled electricity generation scenarios for the western United States

This study explores how emissions from electricity generation in the Western Interconnection region of the U.S. might respond in circa 2030 to contrasting scenarios for fuel prices and greenhouse gas (GHG) emissions fees. We examine spatial and temporal variations in generation mix across the region and year using the PLEXOS unit commitment and dispatch model with a production cost model database adapted from the Western Electricity Coordinating Council. Emissions estimates are computed by combining the dispatch model results with unit-specific, emissions-load relationships. Wind energy displaces natural gas and coal in scenarios with relatively expensive natural gas or with GHG fees. Correspondingly, annual emissions of NOx, SO2, and CO2 are reduced by 20–40% in these cases. NOx emissions, which are a concern as a precursor of ground-level ozone, are relatively high and consistent across scenarios during summer, when peak electricity loads occur and wind resources in the region are comparatively weak. Accounting for the difference in start-up versus stabilized NOx emissions rates for natural gas plants had little impact on region-wide emissions estimates due to the dominant contribution from coal-fired plants, but would be more important in the vicinity of the natural gas units.

Techno-economic assessment of novel vanadium redox flow batteries with large-area cells

The vanadium redox flow battery (VRFB) is a promising electrochemical storage system for stationary megawatt-class applications. The currently limited cell area determined by the bipolar plate (BPP) could be enlarged significantly with a novel extruded large-area plate. For the first time a techno-economic assessment of VRFB in a power range of 1 MW–20 MW and energy capacities of up to 160 MWh is presented on the basis of the production cost model of large-area BPP. The economic model is based on the configuration of a 250 kW stack and the overall system including stacks, power electronics, electrolyte and auxiliaries. Final results include a simple function for the calculation of system costs within the above described scope. In addition, the impact of cost reduction potentials for key components (membrane, electrode, BPP, vanadium electrolyte) on stack and system costs is quantified and validated.

Real earnings management in public vs private firms in the GCC countries: a risk perspective

PurposeThe purpose of this paper is to examine the use of real earnings management by private and public firms in a unique institutional setting, which is the Gulf Cooperation Council (GCC) countries. The paper also compares the level of real earnings management between public and private firms in the GCC area.Design/methodology/approachThe GCC area is a unique setting to investigate the use of real earnings management because of the low enforcement of reporting standards and supervisory rules, lack of sophisticated financial analysis, specialized media tools and high concentration of capital ownership. The authors use different models of real earnings management proposed by Roychowdhury, 2006, cash flow management, productions cost management and discretionary expenses management to examine the use of real earnings management.FindingsThe paper documents evidence consistent with private and public firms using real earnings management to influence their earnings figures. The paper also shows that the level of real earnings management is higher for private firms compared to public firms when cash flow management and discretionary expenses management models are used. The production cost model results show evidence consistent with public firms only engaging in real earnings management through production cost reduction.Research limitations/implicationsThe results of this study might not be applicable to other emerging markets.Practical implicationsThe findings of this study should promote a general understanding of firms’ behavior in unique environment such as GCC countries. Regulators in the GCC region should be aware that real earnings management techniques have been used by firms and that extra caution is required when auditing or analyzing the financial information of private and public firms in the GCC market.Originality/valueThis paper contributes to the literature in many aspects. First, it provides additional evidence on the use of earnings management in unique market contexts outside the USA and Europe. The GCC markets share many common characteristics that make them interesting settings to be investigated. Second, this paper adds more evidence on the use of earnings management between public and private firms. In this regard, the paper adds additional evidence in the discussions proposed by Ball and Shivakumar (2005) and Givoly et al. (2010) who use two competing perspectives to investigate earnings quality in public and private firms: the demand hypothesis and the opportunistic behavior hypothesis.

A mathematical model and optimization of total production cost and quality for a deteriorating production process

Proper production planning can considerably improve a producer’s competitiveness in delivering low-cost products with high quality and short-cycle time. In a deterioration process, the quality may ...

Quantifying the Economic and Grid Reliability Impacts of Improved Wind Power Forecasting

Wind power forecasting is an important tool in power system operations to address variability and uncertainty. Accurately doing so is important to reduce the occurrence and length of curtailment, enhancing market efficiency, and improving the operational reliability of the bulk power system. This research quantifies the value of wind power forecasting improvements in the IEEE 118-bus test system as modified to emulate the generation mixes of Midcontinent, California, and New England independent system operator balancing authority areas. To measure the economic value, a commercially available production cost modeling tool was used to simulate the multitimescale unit commitment (UC) and economic dispatch process for calculating the cost savings and curtailment reductions. To measure the reliability improvements, an in-house tool, Flexible energy scheduling tool for integrating variable generation, was used to calculate the system's area control error and the North American Electric Reliability Corporation Control Performance Standard 2. The approach allowed scientific reproducibility of results and cross validation of the tools. A total of 270 scenarios were evaluated to accommodate the variation of three factors: generation mix, wind penetration level, and wind forecasting improvements. The modified IEEE 118-bus systems utilized 1 year of data at multiple time scales, including the day-ahead UC, 4-h-ahead UC, and real-time dispatch. The value of improved wind power forecasting was found to be strongly tied to the conventional generation mix, existence of energy storage devices, and the penetration level of wind energy. The simulation results demonstrate that wind power forecasting brings clear benefits to power system operations.

Quantifying impacts of heat waves on power grid operation

Climate change is projected to cause an increase in the severity and frequency of extreme weather events such as heat waves and droughts. Such changes present planning and operating challenges and risks to many economic sectors. In the electricity sector, statistics of extreme events in the past have been used to help plan for future peak loads, determine associated infrastructure requirements, and evaluate operational risks, but industry-standard planning tools have yet to be coupled with or informed by temperature models to explore the impacts of the “new normal” on planning studies. For example, high ambient temperatures during heat waves reduce the output capacity and efficiency of gas-fired combustion turbines just when they are needed most to meet peak demands. This paper describes the development and application of a production cost and unit commitment model coupled to high resolution, hourly temperature data and a temperature-dependent load model. The coupled system has the ability to represent the impacts of hourly temperature on load conditions and available capacity and efficiency of combustion turbines, and therefore capture the potential impacts on system reserve and production cost. Ongoing work expands this capability to address the impacts of water availability and temperature on power grid operation.