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paper/paper.md

@@ -20,51 +20,94 @@ bibliography: paper.bib
 # Summary
 Transitioning to a clean energy economy will require expanded energy
 infrastructure. An equitable, or just, transition further requires the
-recognition of the people and communities directly affected by this transition. However, public preferences may be ignored during decision-making processes related energy infrastructure due to a lack of technical rigor or expertise [@johnson:2021]. This challenge is further complicated by the fact that people have and express preferences over many dimensions simultaneously. Multi-objective optimization offers a method to help decision makers and stakeholders understand the problem and analyze tradeoffs among solutions [@liebman:1976]. Although, to date, no multi-objective energy modelling frameworks exist. Open-source multi-objective energy system framework (`osier`) is a Python package for designing and optimizing energy systems across an arbitrary number of dimensions.`osier` was designed to help localized communities articulate their energy preferences in a technical manner without requiring extensive technical expertise. In order to facilitate more robust tradeoff analysis, `osier` generates a set of  technology portfolios, called a Pareto front, with multi-objective optimization using evolutionary algorithms. `osier` also implements a novel algorithm that extends the common modelling-to-generate-alternatives (MGA) algorithm into many dimensions, allowing users to investigate the near-optimal for appealing alternative solutions. In this way, `osier` may address challenges related to procedural and recognition justice.
+recognition of the people and communities directly affected by this transition.
+However, public preferences may be ignored during decision-making processes
+related energy infrastructure due to a lack of technical rigor or expertise
+[@johnson:2021]. This challenge is further complicated by the fact that people
+have and express preferences over many dimensions simultaneously.
+Multi-objective optimization offers a method to help decision makers and
+stakeholders understand the problem and analyze tradeoffs among solutions
+[@liebman:1976]. Although, to date, no multi-objective energy modelling
+frameworks exist. Open-source multi-objective energy system framework (`osier`)
+is a Python package for designing and optimizing energy systems across an
+arbitrary number of dimensions.`osier` was designed to help localized
+communities articulate their energy preferences in a technical manner without
+requiring extensive technical expertise. In order to facilitate more robust
+tradeoff analysis, `osier` generates a set of  technology portfolios, called a
+Pareto front, with multi-objective optimization using evolutionary algorithms.
+`osier` also implements a novel algorithm that extends the common
+modelling-to-generate-alternatives (MGA) algorithm into many dimensions,
+allowing users to investigate the near-optimal for appealing alternative
+solutions. In this way, `osier` may address challenges related to procedural and
+recognition justice.
 
 # Statement of Need
 There are myriad open- and closed-source energy system optimization models
 (ESOMs) available [@pfenninger:2022]. ESOMs can be used for a variety of tasks
 but are most frequently used for prescriptive analyses meant to guide
 decision-makers in planning processes. However, despite the many available
-models, all of these tools share a fundamental characteristic: Optimization over a single economic objective (e.g., total cost or social welfare).
+models, all of these tools share a fundamental characteristic: Optimization over
+a single economic objective (e.g., total cost or social welfare).
 Simultaneously, there is growing awareness of energy justice and calls for its
-inclusion in energy models [@pfenninger:2014, @vagero:2023]. Some studies
+inclusion in energy models [@pfenninger:2014; @vagero:2023]. Some studies
 attempted to incorporate local preferences into energy system design through
 multi-criteria decision analysis (MCDA) and community focus groups
-[@bertsch:2016, @mckenna:2018, @zelt2019]. But these studies rely on tools with pre-defined objectives which are difficult to modify. Without the ability to add objectives that reflect the concerns of a community, the priorities of that community will continue to be secondary to those of modellers and decision makers. A flexible and extensible multi-objective framework that fulfills this need has not yet been developed. `osier` closes this gap.
+[@bertsch:2016; @mckenna:2018; @zelt:2019]. But these studies rely on tools with
+pre-defined objectives which are difficult to modify. Without the ability to add
+objectives that reflect the concerns of a community, the priorities of that
+community will continue to be secondary to those of modellers and decision
+makers. A flexible and extensible multi-objective framework that fulfills this
+need has not yet been developed. `osier` closes this gap.
 
 # Design and Implementation
-In order to run `osier`, users are only required to supply an energy demand time series. Users can optionally provide weather data to incorporate solar or wind energy. The fundamental object in `osier` is an `osier.Technology` object, which contain all of the necessary cost and performance data for different technology classes. `osier` comes pre-loaded with a variety of technologies described in the National Renewable Energy Laboratory's (NREL) Annual Technology Baseline (ATB) dataset[@nationalrenewableenergylaboratory:2023] but users are also able to define their own.
+In order to run `osier`, users are only required to supply an energy demand time
+series. Users can optionally provide weather data to incorporate solar or wind
+energy. The fundamental object in `osier` is an `osier.Technology` object, which
+contain all of the necessary cost and performance data for different technology
+classes. `osier` comes pre-loaded with a variety of technologies described in
+the National Renewable Energy Laboratory's (NREL) Annual Technology Baseline
+(ATB) dataset[@nationalrenewableenergylaboratory:2023] but users are also able
+to define their own.
 
 A set of `osier.Technology` objects, along with user-supplied demand data, can
 be tested independently with the `osier.DispatchModel`. The
-`osier.DispatchModel` is a linear programming model implemented with the `pyomo` library [@hart:2011]. For investment decisions and tradeoff analysis, users can pass their portfolio of `osier.Technology` objects, energy demand, and their desired objectives to the `osier.CapacityExpansion` model, the highest level model in `osier`. The `osier.CapacityExpansion` model is implemented with the multi-objective optimization framework, `pymoo` [@blank:2020]. \autoref{fig:osier-flow} overviews the flow of data through `osier`.
+`osier.DispatchModel` is a linear programming model implemented with the `pyomo`
+library [@hart:2011]. For investment decisions and tradeoff analysis, users can
+pass their portfolio of `osier.Technology` objects, energy demand, and their
+desired objectives to the `osier.CapacityExpansion` model, the highest level
+model in `osier`. The `osier.CapacityExpansion` model is implemented with the
+multi-objective optimization framework, `pymoo` [@blank:2020].
+\autoref{fig:osier-flow} overviews the flow of data through `osier`.
 
 ![The flow of data into and within `osier`
 \label{fig:osier-flow}](osier_flow.png)
 
 ## Key Features
 In addition to being the first and only open-source multi-objective energy
 modelling framework, `osier` has a few key features that further distinguishes
-it from other modelling frameworks. First, since `osier.Technology` objects are Python objects, users can modify values and assumptions, or assign new
+it from other modelling frameworks. First, since `osier.Technology` objects are
+Python objects, users can modify values and assumptions, or assign new
 attributes to the tested technologies. Second, contrary to conventional energy
-system models, `osier` has no required objectives. While users may choose from a variety of pre-defined objectives, they may also declare their own objectives based on any quantifiable metric. The requirements for a bespoke objective are: 
+system models, `osier` has no required objectives. While users may choose from a
+variety of pre-defined objectives, they may also declare their own objectives
+based on any quantifiable metric. The requirements for a bespoke objective are: 
 
 1. The first argument must be a list of `osier.Technology` objects.
 2. The second argument must be the results from an `osier.DispatchModel`. But
    this may be a simple placeholder with a default value of `None` as shown
    below.
 3. The function must return a numerical value.
-4. The final (implicit) requirement, is that all `osier.Technology` objects possess the attribute being optimized.
+4. The final (implicit) requirement, is that all `osier.Technology` objects
+   possess the attribute being optimized.
 
 These two features acknowledge that a modeler cannot know *a priori* all
 possible objectives or parameters of interest. Allowing users to define their
 own objectives and modify technology objects (or simply build their own by
 inheriting from the `osier.Technology` class) accounts for this limitation and
-expands the potential for incorporating localized preferences.
-
-Lastly, in order to account for unmodeled or unmodelable objectives, `osier` extends the conventional MGA algorithm into N-dimensions by using a farthest-first-traversal in the design space.
+expands the potential for incorporating localized preferences.Lastly, in order
+to account for unmodeled or unmodelable objectives, `osier` extends the
+conventional MGA algorithm into N-dimensions by using a farthest-first-traversal
+in the design space.
 
 ## Documentation
 
@@ -73,7 +116,12 @@ Lastly, in order to account for unmodeled or unmodelable objectives, `osier` ext
 
 # Acknowledgements
 
-Samuel Dotson was supported by the Nuclear Regulatory Commission Fellowship program. This research was part of the Advanced Reactors and Fuel Cycles (ARFC) group in the Department of Nuclear, Plasma, and Radiological Engineering (NPRE) at the University of Illinois Urbana-Champaign. Additionally, Samuel Dotson was supported by the Felix T. Adler Fellowship through NPRE. The views and opinions expressed in this paper are those of the author alone.
+Samuel Dotson was supported by the Nuclear Regulatory Commission Fellowship
+program. This research was part of the Advanced Reactors and Fuel Cycles (ARFC)
+group in the Department of Nuclear, Plasma, and Radiological Engineering (NPRE)
+at the University of Illinois Urbana-Champaign. Additionally, Samuel Dotson was
+supported by the Felix T. Adler Fellowship through NPRE. The views and opinions
+expressed in this paper are those of the author alone.
 
 # References