Combined-Cycle Power Plant Maintenance Costs

Grace, Dale

doi:10.1115/gt2008-51357

Cited by 5 publications

(4 citation statements)

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“…At a minimum, cost of failure is the sum total of cost of repairs (parts, materials, and labor) and downtime (lost opportunity of power generation) and can be much higher in certain cases such as major accidents [13,14]. Its exact value is not that important from a conceptual analysis perspective (of the order of $5000/MW-yr for E and F-class plants based on the study in Ref.…”

Section: (7)mentioning

confidence: 99%

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An Expanded Cost of Electricity Model for Highly Flexible Power Plants

Gülen

Mazumder

2012

Journal of Engineering for Gas Turbines and Power

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An Expanded Cost of Electricity Modei for Higiiiy Fiexibie Power PiantsCost of electricity (COE) is the most widely used metric to quantify the cost-performance trade-off involved in comparative analysis of competing electric power generation technologies. Unfortunately, the currently accepted formulation of COE is only applicable to comparisons of power plant options with ihe same annual electric generation (kilowatt-hours) and the same technology as defined by reliability, availability, and operability. Such a formulation does not introduce a big error into the COE analysis when the objective is simply to compare two or more base-loaded power plants of the same technology (e.g., natural gas fired gas turbine simple or combined cycle, coal fired conventional boiler steam turbine, etc.) and the same (or nearly the same) capacity. However, comparing even the same technology class power plants, especially highly flexible advanced gas turbine combined cycle units with cyclic duties, comprising a high number of daily starts and stops in addition to emissions-compliant low-load operation to accommodate the intermittent and uncertain load regimes of renewable power generation (mainly wind and solar) requires a significant overhaul of the basic COE formula. This paper develops an expanded COE formulation by incorporating crucial power plant operability and maintainability characteristics such as reliability, unrecoverable degradation, and maintenance factors as well as emissions into the mix. The core impact of duty cycle on the plant performance is handled via effective output and efficiency utilizing basic performance correction curves. The impact of plant start and load ramps on the effective performance parameters is included. Differences in reliability and total annual energy generation are handled via energy and capacity replacement terms. The resulting expanded formula, while rigorous in development and content, is still simple enough for most feasibility study type of applications. Sample calculations clearly reveal that inclusion (or omission) of one or more of these factors in the COE evaluation, however, can dramatically swing the answer from one extreme to the other in some cases.

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Section: (7)mentioning

confidence: 99%

“…Also in Eq. (13), Ph is the annual load and ambient-weighted average unit output to be used as a basis in COE comparison. For multiple unit comparisons, it can be set to the highest P among the alternatives.…”

Section: System Considerationsmentioning

confidence: 99%

An Expanded Cost of Electricity Model for Highly Flexible Power Plants

Gülen

Mazumder

2012

Journal of Engineering for Gas Turbines and Power

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“…The authors clarify that to reduce the operating costs, the engines are demanded to operate longer and reduce maintenance costs. For instance, the costs of maintenance for gas fired combined cycle plants can reach 50% of the total O&M costs [3] and represent 7% of the overall project cash flow. Additionally, statistic studies conducted over 3000 E and F class engines concluded that the unplanned maintenance cost can reach 8% of the O&M costs, or 2% of net revenue income and the loss of revenue can reach the 15% of the O&M cost or 5% of net revenue income [4].…”

Section: Introductionmentioning

confidence: 99%

Comparing Different Schemes in a Combined Technique of Kalman Filter, Artificial Neural Network and Fuzzy Logic for Gas Turbines Online Diagnostics

Togni

Nikolaidis

Sampath

2022

Volume 2: Coal, Biomass, Hydrogen, and Alternative Fuels; Controls, Diagnostics, and Instrumentation; Steam Turbine

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The paper presents research on the online performance-based diagnostics by implementing a novel methodology, which is based on the combination of Kalman Filter, Artificial Neural Network, Neuro-Fuzzy Logic and Fuzzy Logic. These methods are proposed to improve the success rate, increase the flexibility, and allow the detection of single and multiple failures. The methodology is applied to a 2-shaft industrial gas turbine engine for the automated early detection of single and multiple failures with the presence of measurement noise. The methodology offers performance prediction and the possibility of utilizing multiple schemes for the online diagnostics. The architecture leads to three possible schemes. The first scheme includes the base methodology and enables Kalman Filter for data filtering, Artificial Neural Network for the component efficiency prediction, the Neuro-Fuzzy logic for the failure quantification and the Fuzzy Logic for the failure classification. For this scheme, a performance simulation tool (Turbomatch) is used to calculate the thermodynamic baseline. The second scheme replaces Turbomatch with the Artificial Neural Network, that is used to calculate the deteriorated efficiencies and the reference efficiencies. The third scheme is identical to the first one but excludes the shaft power measurements, which are not available in aero engines or might not be usable for some power plant configurations. The paper compares the performance of the three methodologies, with the presence of measurement noise (0.4% reference noise and 2.0% reference noise), and 24 types of random single and multiple failures, with variable magnitude. The first methodology has been already presented by Togni et al. [10], whereas the other two methodologies and results are part of the PhD thesis presented by Togni [18] and they extend the applicability of the method. The success rate, targeting the correct detection of the of the failure magnitude ranges between 92% and 100% without measurement noise and ranges between 66% and 83% with measurement noise. Instead, the success rate of the classification, targeting the correct detection of the type of failure ranges between 93% and 100% without measurement noise and between 85% and 100% with measurement noise.

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“…Since the initial work reported in 2001 and refinements presented in 2008 [2], the statistical basis and costing methodology have been expanded. Furthermore, significantly more experience has been gained from the operation of F-Class gas turbines, particularly as it relates to unplanned maintenance events.…”

Section: Introductionmentioning

confidence: 99%

Risk-Based Assessment of Unplanned Outage Events and Costs for Combined-Cycle Plants

Grace

Christiansen²

2013

Journal of Engineering for Gas Turbines and Power

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Unexpected outages and maintenance costs reduce plant availability and can consume signiflcant resources to restore the unit to service. This paper describes a survey and analysis of unplanned outage events for 388 combined-cycle plants, based on 164 D/E-Class and 224 F-Class gas turbines. The 15-year timeframe included over 3000 unityears of data. The causes and durations of forced outages and unscheduled maintenance were identified, and overall reliability and availability were established for each class of plant. Costs to restore the unit to sei-vice for each main outage cause were estimated, as were net revenues lost due to unplanned outages. A statistical approach to estimated costs and lost revenues provides a risk-based means to quantify the impact of unplanned events on plant cash flow as a function of class of gas turbine and plant subsystem. The risk-based assessment defines the range of probable costs of unplanned outage events.

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Combined-Cycle Power Plant Maintenance Costs

Cited by 5 publications

References 0 publications

An Expanded Cost of Electricity Model for Highly Flexible Power Plants

An Expanded Cost of Electricity Model for Highly Flexible Power Plants

Comparing Different Schemes in a Combined Technique of Kalman Filter, Artificial Neural Network and Fuzzy Logic for Gas Turbines Online Diagnostics

Risk-Based Assessment of Unplanned Outage Events and Costs for Combined-Cycle Plants

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