Optimization of carbon-capture-enabled coal-gas-solar power generation

Brodrick, Philip G.; Kang, Charles A.; Brandt, Adam R.; Durlofsky, Louis J.

doi:10.1016/j.energy.2014.11.003

Cited by 43 publications

(15 citation statements)

References 19 publications

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“…Nonetheless, the main weakness of the genetic algorithms is the uncertainty in their capability to identify the optimum condition; ie, obtaining the global optimum is not guaranteed by genetic algorithms. Thus, a hybrid optimization approach is taken into consideration in which two distinctive optimization methods are carried out in a successive manner …”

Section: Resultsmentioning

confidence: 99%

“…Thus, a hybrid optimization approach is taken into consideration in which two distinctive optimization methods are carried out in a successive manner. 80 For the second stage of the hybrid optimization, the recommended method by Ramos and Ramos 81 based on Powell algorithm 82 is selected. The algorithm implements a line search approach to perform the optimization of power plants.…”

Section: New Power Plant Installment Economic Analysismentioning

confidence: 99%

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A critical assessment of thermo‐economic analyses of different air bottoming cycles for waste heat recovery

Saghafifar

Gadalla

2018

Int J Energy Res

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Summary Steam turbine cycle's low operating temperature makes it suitable for waste heat recovery applications. Even though conventional combined cycles, ie, topping gas turbine and bottoming steam turbine cycles, are thermodynamically efficient, they are not the most economical alternatives for power generation with capacities less than 50 MWe. A recently proposed alternative is to utilize a bottoming gas turbine cycle in form of an air bottoming cycle. In this study, an overview of air bottoming cycle is presented. Based on the discussed studies, it is decided to further evaluate the merits of water injection in the bottoming cycle air stream by using either a humidifier or an air saturator. Thermo‐economic analysis and optimization are performed to evaluate simple and water injected air bottoming cycles against steam bottoming cycles. Results indicate that conventional combined cycles can achieve the highest thermal efficiency of about 48%. While water injected air bottoming cycle with air saturator is the most cost effective combined cycle configuration and most efficient air bottoming cycle with levelized cost of electricity and energy efficiency of 64.41 US$/MWh and 39%–40%, respectively, followed by the water injected air bottoming cycle with humidifier and simple air bottoming cycle with reported levelized cost of electricity of 65.75 US$/MWh, 66.36 US$/MWh, respectively. Steam bottoming cycle has the highest levelized cost of electricity of 68.88 US$/MWh.

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Section: Resultsmentioning

confidence: 99%

Section: New Power Plant Installment Economic Analysismentioning

confidence: 99%

A critical assessment of thermo‐economic analyses of different air bottoming cycles for waste heat recovery

Saghafifar

Gadalla

2018

Int J Energy Res

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“…In the 'full-physics' representation that was the focus in Kang et al [19], the CO 2 capture system was modeled using a rate-based Aspen Plus process simulation (or a statistical fit of Aspen Plus model output). In the 'simplified-capture' representation, which was employed in Kang et al [14] and Brodrick et al [21], the CO 2 capture system was represented using a variable for system capacity and constant parameters for a small number of performance specifications. In [19] we showed that the two modeling approaches provided similar high-level designs and objective function values for the cases considered, which suggests that adequate optimization results are obtainable at much less computational effort using the simplified-capture representation.…”

Section: Introductionmentioning

confidence: 99%

“…Finally, we note that the method applied here in the context of a gas-fired CO 2 capture retrofit is quite general, and it could be used for a wide range of system configurations and CO 2 capture technologies. As an example, an earlier form of the model used here was modified to consider integration with solar thermal energy [21]. This paper proceeds as follows.…”

Section: Introductionmentioning

confidence: 99%

Assessment of advanced solvent-based post-combustion CO2 capture processes using a bi-objective optimization technique

et al. 2016

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The optimized performance of two advanced CO 2 capture processes is compared to that of a monoethanolamine (MEA) baseline for a gas-powered CO 2 capture retrofit of an existing coal-fired facility. The advanced temperature-swing processes utilize piperazine and mixed-salt solvents. The mixed-salt treatment involves the use of ammonia for CO 2 absorption and potassium carbonate primarily to control ammonia slip. The processes are represented in terms of energy duty requirements within a modular heat integration code developed for CO 2 capture modeling and optimization. The model includes a baseload coal plant, a gas-fired subsystem containing gas turbines and a heat recovery steam generator (HRSG), and a CO 2 capture facility. A formal bi-objective optimization procedure is applied to determine the design (e.g., detailed HRSG components and pressure levels, gas turbine capacity, CO 2 capture capacity) and time-varying operations of the facility to simultaneously maximize net present value (NPV) and minimize total capital requirement (TCR), while meeting a maximum CO 2 emission intensity constraint. For a realistic scenario constructed using historical data, optimization results indicate that both advanced processes outperform MEA in both objectives, and the mixed-salt process in turn outperforms the piperazine process. Specifically, for the scenario considered, the base case mixed-salt process achieves 16% greater NPV and 14% lower TCR than the MEA process, and 10% greater NPV and 5% lower TCR than the piperazine process. A five-case sensitivity study of the mixed-salt process indicates that it is competitive with the piperazine process and consistently outperforms the MEA process.

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“…Recently, the authors and other groups discussed the accuracy of aggregation methods in the domain of the objective function: Green et al (2014) discuss the difference in resulting cost for an electrical system with typical periods compared with the original time series. Brodrick et al (2015) generate typical periods using k-means, evaluate the accuracy of aggregation as difference in the objective function value, and analyze various numbers of typical periods. For the optimal design of building energy systems, Schütz et al (2016) compare several clustering methods for typical periods by both time-series representation and differences in annual costs.…”

Section: Introductionmentioning

confidence: 99%

Typical Periods for Two-Stage Synthesis by Time-Series Aggregation with Bounded Error in Objective Function

et al. 2018

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Two-stage synthesis problems simultaneously consider here-and-now decisions (e.g., optimal investment) and wait-and-see decisions (e.g., optimal operation). The optimal synthesis of energy systems reveals such a two-stage character. The synthesis of energy systems involves multiple large time series such as energy demands and energy prices. Since problem size increases with the size of the time series, synthesis of energy systems leads to complex optimization problems. To reduce the problem size without loosing solution quality, we propose a method for time-series aggregation to identify typical periods. Typical periods retain the chronology of time steps, which enables modeling of energy systems, e.g., with storage units or start-up cost. The aim of the proposed method is to obtain few typical periods with few time steps per period, while accurately representing the objective function of the full time series, e.g., cost. Thus, we determine the error of time-series aggregation as the cost difference between operating the optimal design for the aggregated time series and for the full time series. Thereby, we rigorously bound the maximum performance loss of the optimal energy system design. In an initial step, the proposed method identifies the best length of typical periods by autocorrelation analysis. Subsequently, an adaptive procedure determines aggregated typical periods employing the clustering algorithm k-medoids, which groups similar periods into clusters and selects one representative period per cluster. Moreover, the number of time steps per period is aggregated by a novel clustering algorithm maintaining chronology of the time steps in the periods. The method is iteratively repeated until the error falls below a threshold value. A case study based on a real-world synthesis problem of an energy system shows that time-series aggregation from 8,760 time steps to 2 typical periods with each 2 time steps results in an error smaller than the optimality gap of the synthesis problem (2%). This corresponds to a reduction of the number time steps and thus a reduction of the size of the synthesis problem by a factor of 1,000 with excellent accuracy in cost estimation. Thus, the proposed method enables an efficient and accurate synthesis of energy systems.

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Optimization of carbon-capture-enabled coal-gas-solar power generation

Cited by 43 publications

References 19 publications

A critical assessment of thermo‐economic analyses of different air bottoming cycles for waste heat recovery

A critical assessment of thermo‐economic analyses of different air bottoming cycles for waste heat recovery

Assessment of advanced solvent-based post-combustion CO2 capture processes using a bi-objective optimization technique

Typical Periods for Two-Stage Synthesis by Time-Series Aggregation with Bounded Error in Objective Function

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