Robust Operational Optimization of a Typical micro Gas Turbine

Paepe, Ward De; Coppitters, Diederik; Abraham, Simon Michel; Tsirikoglou, Panagiotis; Ghorbaniasl, Ghader; Contino, Francesco

doi:10.1016/j.egypro.2019.01.549

Cited by 17 publications

(11 citation statements)

References 8 publications

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“…As in this specific uncertainty characterization the model output is characterized by a probability box, the mean and standard deviation of the upper probability bound of the probability box are selected, to guarantee a robust prediction on the LCOE [25]. The sparse PCE method is coupled to the Nondominated Sorting Genetic Algorithm (NSGA-II) to find the set of designs that presents the trade-off between minimizing the mean and standard deviation of the LCOE [26][27][28]. Hence, for every evaluated design, the sparse PCE method is applied to quantify the statistical moments.…”

Section: Robust Design Optimizationmentioning

confidence: 99%

Robust design optimization of a renewable-powered demand with energy storage using imprecise probabilities

2021

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During renewable energy system design, parameters are generally fixed or characterized by a precise distribution. This leads to a representation that fails to distinguish between uncertainty related to natural variation (i.e. future, aleatory uncertainty) and uncertainty related to lack of data (i.e. present, epistemic uncertainty). Consequently, the main driver of uncertainty and effective guidelines to reduce the uncertainty remain undetermined. To assess these limitations on a grid-connected household supported by a photovoltaic-battery system, we distinguish between present and future uncertainty. Thereafter, we performed a robust design optimization and global sensitivity analysis. This paper provides the optimized designs, the main drivers of the variation in levelized cost of electricity and the effect of present uncertainty on these drivers. To reduce the levelized cost of electricity variance for an optimized photovoltaic array and optimized photovoltaic-battery design, improving the determination of the electricity price for every specific scenario is the most effective action. For the photovoltaic-battery robust design, the present uncertainty on the prediction accuracy of the electricity price should be addressed first, before the most effective action to reduce the levelized cost of electricity variance can be determined. Future work aims at the integration of a heat demand and hydrogen-based energy systems.

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Section: Robust Design Optimizationmentioning

confidence: 99%

Robust design optimization of a renewable-powered demand with energy storage using imprecise probabilities

2021

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“…Pareto set of solutions). Another study based on this RDO has already been carried out by our research group in [39]. The NSGA-II algorithm starts from an initial population, out of which it creates an offspring based on crossover and mutation rules.…”

Section: Non-dominated Sorting Genetic Algorithm II (Nsga Ii)mentioning

confidence: 99%

Surrogate-Assisted Modeling and Robust Optimization of a Micro Gas Turbine Plant With Carbon Capture

Giorgetti

Coppitters

Contino

et al. 2019

Journal of Engineering for Gas Turbines and Power

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The growing share of wind and solar power in the total energy mix has caused severe problems in balancing the electrical power production. Consequently, in the future, all fossil fuel-based electricity generation will need to be run on a completely flexible basis. Microgas turbines (mGTs) constitute a mature technology which can offer such flexibility. Even though their greenhouse gas emissions are already very low, stringent carbon reduction targets will require them to be completely carbon neutral: this constraint implies the adoption of postcombustion carbon capture (CC) on these energy systems. Despite this attractive solution, an in-depth study along with a robust optimization of this system has not yet been carried out. Hence, in this paper, a typical mGT with exhaust gas recirculation has been coupled with an amine-based CC plant and simulated using the software aspenplus. A rigorous rate-based simulation of the CO2 absorption and desorption in the CC unit offers an accurate prediction; however, its time complexity and convergence difficulty are severe limitations for a stochastic optimization. Therefore, a surrogate-based optimization approach has been used, which makes use of a Gaussian process regression (GPR) model, trained using the aspenplus data, to quickly find operating points of the plant at a very low computational cost. Using the validated surrogate model, a stochastic optimization has been carried out. As a general result, the analyzed power plant proves to be intrinsically very robust, even when the input variables are affected by strong uncertainties.

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“…This can be done iteratively by investigating each system separately or as one system in a global optimisation process. To optimise the design of such complex systems for multiple objectives, metaheuristic optimisation strategies are favoured, such as Particle Swarm optimisation [27] and Nondominated Sorting Genetic Algorithm (NSGA-II) [28][29][30]. Rao et al performed a multi-objective optimisation via a genetic algorithm of double-acting hybrid active magnetic thrust bearings, subject to geometric, electrical and control constraints [31].…”

Section: Introductionmentioning

confidence: 99%

Multi-Fidelity Design Optimisation of a Solenoid-Driven Linear Compressor

et al. 2020

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Improved management and impermeability of refrigerants is a leading solution to reverse global warming. Therefore, crank-driven reciprocating refrigerator compressors are gradually replaced by more efficient, oil-free and hermetic linear compressors. However, the design and operation of an electromagnetic actuator, fitted on the compression requirements of a reciprocating linear compressor, received limited attention. Current research mainly focuses on the optimisation of short stroke linear compressors, while long stroke compressors benefit from higher isentropic and volumetric efficiencies. Moreover, designing such a system focuses mainly on the trade-off between number of copper windings and the current required, due to the large computational cost of performing a full geometric design optimisation based on a Finite Element Method. Therefore, in this paper, a computationally-efficient, multi-objective design optimisation for six geometric design parameters has been applied on a solenoid driven linear compressor with a stroke of 44.2 mm. The proposed multi-fidelity optimisation approach takes advantage of established models for actuator optimisation in mechatronic applications, combined with analytical equations established for a solenoid actuator to increase the overall computational efficiency. This paper consists of the multi-fidelity optimisation algorithm, the analytic model and Finite Element Method of a solenoid and the optimised designs obtained for optimised power and copper volume, which dominates the actuator cost. The optimisation results illustrate a trade-off between minimising the peak power and minimising the volume of copper windings. Considering this trade-off, an intermediate design is highlighted, which requires 33.3% less power, at the expense of an increased copper volume by 5.3% as opposed to the design achieving the minimum copper volume. Despite that the effect of the number of windings on the input current remains a dominant design characteristic, adapting the geometric parameters reduces the actuator power requirements significantly as well. Finally, the multi-fidelity optimisation algorithm achieves a 74% reduction in computational cost as opposed to an entire Finite Element Method optimisation. Future work focuses on a similar optimisation approach for a permanent magnet linear actuator.

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Robust Operational Optimization of a Typical micro Gas Turbine

Cited by 17 publications

References 8 publications

Robust design optimization of a renewable-powered demand with energy storage using imprecise probabilities

Robust design optimization of a renewable-powered demand with energy storage using imprecise probabilities

Surrogate-Assisted Modeling and Robust Optimization of a Micro Gas Turbine Plant With Carbon Capture

Multi-Fidelity Design Optimisation of a Solenoid-Driven Linear Compressor

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