Merge pull request #14 from danielskatz/patch-1

Update paper.md

Author: DCoppitters

paper/paper.md

@@ -53,12 +53,12 @@ hydrogen can be converted back into electricity (power-to-power),
 it can be used to produce low-carbon fuels (power-to-fuel),
 and it can be used to fuel hydrogen vehicles (power-to-mobility).
 The performance of these hydrogen-based energy systems is subject to uncertainties, 
-such as the uncertainty on the solar irradiance, the energy consumption of hydrogen-powered buses and the price of grid electricity.
+such as the uncertainty on the solar irradiance, the energy consumption of hydrogen-powered buses, and the price of grid electricity.
 Disregarding these uncertainties in the design process can result in a drastic mismatch between simulated and real-world performance, 
 and thus lead to a *kill-by-randomness* of the system.
 The *Robust design optimization of renewable Hydrogen and dErIved energy cArrier systems* (RHEIA) framework provides a robust design optimization pipeline 
 that considers real-world uncertainties and yields robust designs, i.e., designs with a performance less sensitive to these uncertainties.
-Moreover, RHEIA includes models to evaluate hydrogen's techno-economic and environmental performance in a power-to-fuel, power-to-power and power-to-mobility context.
+Moreover, RHEIA includes models to evaluate hydrogen's techno-economic and environmental performance in a power-to-fuel, power-to-power, and power-to-mobility context.
 When combined, RHEIA unlocks the robust designs for hydrogen-based energy systems.
 As RHEIA considers the system models as a black box, the framework can be applied to existing open-source and closed-source models.
 To illustrate, an interface with the [EnergyPLAN](https://www.energyplan.eu/) software is included in the framework. 
@@ -68,22 +68,22 @@ To illustrate, an interface with the [EnergyPLAN](https://www.energyplan.eu/) so
 
 Incorporating hydrogen is still an anomaly in design optimization studies of renewable energy systems [@Eriksson2017]. 
 Moreover, the optimization is often performed under the assumption of deterministic parameters (i.e., fixed, free from inherent variation).
-Considering fixed values for model parameters in design optimization yields designs that might be sensitive -- the real issue is that we cannot know-- to real-world uncertainties
+Considering fixed values for model parameters in design optimization yields designs that might be sensitive -- the real issue is that we cannot know -- to real-world uncertainties
 and results in a drastic mismatch between simulated and actual performances.
-In fields different from energy systems, e.g., structural mechanics, aerospace and automobile applications, 
-Robust Design Optimization (RDO) yielded robust designs by minimizing the variance on the performance [@orosz2020robust].
+In fields different from energy systems, e.g., structural mechanics, aerospace, and automobile applications, 
+Robust Design Optimization (RDO) yields robust designs by minimizing the variance on the performance [@orosz2020robust].
 Consequently, alternative design solutions were proposed that provide the least sensitive performance to the random environment.
 To ensure the computational tractability of RDO, surrogate modelling techniques achieve a promising computational efficiency
-to quantify the mean and variance on the performance. Nevertheless, applications of such surrogate-assisted robust design optimization techniques are limited [@Chatterjee2017].
+to quantify the mean and variance of the performance. Nevertheless, applications of such surrogate-assisted robust design optimization techniques are limited [@Chatterjee2017].
 To fill these research gaps, RHEIA provides a multi-objective RDO algorithm,
 for which the uncertainty quantification is performed through a Polynomial Chaos Expansion (PCE) surrogate modelling technique.
-In addition, RHEIA includes Python-based models for relevant valorization pathways of hydrogen: power-to-fuel, power-to-power and power-to-mobility.
+In addition, RHEIA includes Python-based models for relevant valorization pathways of hydrogen: power-to-fuel, power-to-power, and power-to-mobility.
 The significant techno-economic and environmental uncertainties for these models are characterized based on scientific literature, 
 and a method is included to gather climate data and demand data for the location of interest.
 Finally, RHEIA allows connecting your own models to the RDO and uncertainty quantification algorithms as well.   
 
 Simulation models that include the evaluation of hydrogen-based energy systems exist,
-e.g., [INSEL](https://insel.eu/en/home_en.html), [EnergyPLAN](https://www.energyplan.eu/) and [TRNSYS](http://www.trnsys.com/).
+e.g., [INSEL](https://insel.eu/en/home_en.html), [EnergyPLAN](https://www.energyplan.eu/), and [TRNSYS](http://www.trnsys.com/).
 Despite their extensive component model libraries, these simulation models lack an optimization feature.
 [HOMER Energy](https://www.homerenergy.com/products/pro/index.html) includes an optimization algorithm to design hybrid microgrids, including hydrogen system component models.
 In Python, [Calliope](https://www.callio.pe/) [@pfenninger2018calliope] considers the optimization of multi-scale energy system models, where hydrogen is regarded as a fuel in advanced gas turbines.
@@ -117,4 +117,4 @@ epistemic and aleatory uncertainty on a parameter. For example, we performed an
 
 The first author acknowledges the support from the Belgian federal Energy Transition Fund, project DRIVER.
 
-# References
+# References