How can power-to-ammonia be robust? Optimization of an ammonia synthesis plant powered by a wind turbine considering operational uncertainties

Verleysen, Kevin; Coppitters, Diederik; Parente, Alessandro; Paepe, Ward De; Contino, Francesco

doi:10.1016/j.fuel.2020.117049

Cited by 59 publications

(30 citation statements)

References 37 publications

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“…Electrofuels have the ability to couple energy and non-energy sectors [11]. For instance, electricity produced in excess from VRES can be converted in ammonia through the Haber-Bosch process and subsequently transformed into fertiliser-coupling the power and industry sectors [12]. Gas networks present much more storage potential than electrical networks (e.g., 50 times more in Germany and 300 times more in France) [13].…”

Section: Introductionmentioning

confidence: 99%

“…Coppitters et al [37] constructed a PCE on a photovoltaic-electrolyser system design and, based on the Sobol' indices, bulk manufacturing of the electrolyser and more demonstration projects are suggested to reduce the variance on the levelised cost of hydrogen. Similarly, Verleysen et al [12] quantified the Sobol' indices from a PCE on a power-to-ammonia system and highlighted that an accurate flow meter and improving the temperature control in an ammonia reactor are the main actions to render the performance more robust.…”

Section: Introductionmentioning

confidence: 99%

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The Role of Electrofuels under Uncertainties for the Belgian Energy Transition

et al. 2021

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Wind and solar energies present a time and space disparity that generally leads to a mismatch between the demand and the supply. To harvest their maximum potentials, one of the main challenges is the storage and transport of these energies. This challenge can be tackled by electrofuels, such as hydrogen, methane, and methanol. They offer three main advantages: compatibility with existing distribution networks or technologies of conversion, economical storage solution for high capacity, and ability to couple sectors (i.e., electricity to transport, to heat, or to industry). However, the level of contribution of electric-energy carriers is unknown. To assess their role in the future, we used whole-energy system modelling (EnergyScope Typical Days) to study the case of Belgium in 2050. This model is multi-energy and multi-sector. It optimises the design of the overall system to minimise its costs and emissions. Such a model relies on many parameters (e.g., price of natural gas, efficiency of heat pump) to represent as closely as possible the future energy system. However, these parameters can be highly uncertain, especially for long-term planning. Consequently, this work uses the polynomial chaos expansion method to integrate a global sensitivity analysis in order to highlight the influence of the parameters on the total cost of the system. The outcome of this analysis points out that, compared to the deterministic cost-optimum situation, the system cost, accounting for uncertainties, becomes higher (+17%) and twice more uncertain at carbon neutrality and that electrofuels are a major contribution to the uncertainty (up to 53% in the variation of the costs) due to their importance in the energy system and their high uncertainties, their higher price, and uncertainty.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Role of Electrofuels under Uncertainties for the Belgian Energy Transition

et al. 2021

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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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“…A wide range of direct search methods have been developed from heuristic optimization, such as genetic algorithms, evolutionary programming, differential evolution, genetic programming, evolutionary strategy, particle swarm optimization, and artificial bee colonies [36,37]; however, a theoretical analysis of convergence is not available, which is a major shortcoming [16,38]. A variety of approaches considering uncertainties in the optimization of chemical processes have been reported in the open literature [39,40].…”

Section: Introductionmentioning

confidence: 99%

A TITO Control Strategy to Increase Productivity in Uncertain Exothermic Continuous Chemical Reactors

2021

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In this manuscript, a two-input two-output (TITO) control strategy for an exothermic continuous chemical reactor is presented. The control tasks of the continuous chemical reactor are related to temperature regulation by a standard proportional-integral (PI) controller. The selected set point increases reactor productivity due to the temperature effect and prevents potential thermal runaway, and the temperature increases until it reaches isothermal operating conditions. Then, an optimal controller is activated to increase the mass reactor productivity. The optimal control strategy is based on a Euler-Lagrange framework, in which the corresponding Lagrangian is based on the model equations of the reactor, and the optimal controller is coupled with an uncertainty estimator to infer the unknown terms required by the proposed controller. As a benchmark, a continuous stirred tank reactor (CSTR) with a Van de Vusse chemical reaction is considered as an application case study. Notably, the proposed methodology is generally applicable to any continuous stirred tank reactor. The results of numerical experiments verify the satisfactory performance of the proposed control strategy.

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How can power-to-ammonia be robust? Optimization of an ammonia synthesis plant powered by a wind turbine considering operational uncertainties

Cited by 59 publications

References 37 publications

The Role of Electrofuels under Uncertainties for the Belgian Energy Transition

The Role of Electrofuels under Uncertainties for the Belgian Energy Transition

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

A TITO Control Strategy to Increase Productivity in Uncertain Exothermic Continuous Chemical Reactors

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