diff --git a/paper.bib b/paper.bib index 548c83d..17f7469 100644 --- a/paper.bib +++ b/paper.bib @@ -1,97 +1,85 @@ - -@online{pfenninger_openmod_nodate, - title = {openmod - Open Energy Modelling Initiative}, - url = {https://www.openmod-initiative.org/}, - abstract = {The Open Energy Modelling (openmod) Initiative promotes open energy modelling across the world. - -Energy models are widely used for policy advice and research. They serve to help answer questions on energy policy, decarbonization, and transitions towards renewable energy sources. Currently, most energy models are black boxes – even to fellow researchers. -“Open” refers to model source code that can be studied, changed and improved as well as freely available energy system data. - -We believe that more openness in energy modelling increases transparency and credibility, reduces wasteful double-work and improves overall quality. This allows the community to advance the research frontier and gain the highest benefit from energy modelling for society. - -We, energy modelers from various institutions, want to promote the idea and practice of open energy modelling among fellow modelers, research institutions, funding bodies, and recipients of our work.}, - titleaddon = {openmod-initiative}, - author = {Pfenninger, Stefan and Schlect, Ingmar and Trondle, Tim and Brown, Tom}, - urldate = {2022-12-13}, - file = {openmod - Open Energy Modelling Initiative:/Users/samdotson/Zotero/storage/8FWR7W4F/www.openmod-initiative.org.html:text/html}, +@article{liebmanSimpleMindedObservationsRole1976, + title = {Some {{Simple-Minded Observations}} on the {{Role}} of {{Optimization}} in {{Public Systems Decision-Making}}}, + author = {Liebman, Jon C.}, + year = {1976}, + month = aug, + journal = {Interfaces}, + volume = {6}, + number = {4}, + pages = {102--108}, + publisher = {INFORMS}, + issn = {0092-2102}, + doi = {10.1287/inte.6.4.102}, + urldate = {2023-01-18}, + abstract = {Because public systems problems are frequently ill-defined and have fuzzy constraints and vague multiple objectives, their solution by means of formal optimization models is not widely accepted. This paper will explore the modes (both useful and otherwise) in which optimization has been applied to such problems. The need for optimization models which somehow ``fit'' the decision-makers' methods and backgrounds will be discussed. Several other aspects of public systems optimization will be considered, in a somewhat random fashion. This paper will raise more questions than answers.} } -@article{liebman_simple-minded_1976, - title = {Some Simple-Minded Observations on the Role of Optimization in Public Systems Decision-Making}, - volume = {6}, - issn = {0092-2102}, - url = {https://pubsonline.informs.org/doi/10.1287/inte.6.4.102}, - doi = {10.1287/inte.6.4.102}, - abstract = {Because public systems problems are frequently ill-defined and have fuzzy constraints and vague multiple objectives, their solution by means of formal optimization models is not widely accepted. This paper will explore the modes (both useful and otherwise) in which optimization has been applied to such problems. The need for optimization models which somehow “fit” the decision-makers' methods and backgrounds will be discussed. Several other aspects of public systems optimization will be considered, in a somewhat random fashion. This paper will raise more questions than answers.}, - pages = {102--108}, - number = {4}, - journaltitle = {Interfaces}, - author = {Liebman, Jon C.}, - urldate = {2023-01-18}, - date = {1976-08}, - note = {Publisher: {INFORMS}}, +@article{mckennaCombiningLocalPreferences2018, + title = {Combining Local Preferences with Multi-Criteria Decision Analysis and Linear Optimization to Develop Feasible Energy Concepts in Small Communities}, + author = {McKenna, R. and Bertsch, V. and Mainzer, K. and Fichtner, W.}, + year = {2018}, + month = aug, + journal = {European Journal of Operational Research}, + series = {Community {{Operational Research}}: {{Innovations}}, Internationalization and Agenda-Setting Applications}, + volume = {268}, + number = {3}, + pages = {1092--1110}, + issn = {0377-2217}, + doi = {10.1016/j.ejor.2018.01.036}, + urldate = {2024-02-25}, + abstract = {Decentralised community energy resources are often abundant in smaller, more rural communities. Such communities often lack the capacity to develop extensive energy concepts and thus to exploit these resources in a consistent way. This paper presents an integrated participatory approach to developing feasible energy concepts for small communities. The novelty lies in the combination of methods, the consideration of uncertainties, and the application to an exemplary municipality in Germany. Stakeholder workshops are combined with energy modelling and multi-criteria decision analysis (MCDA), and a high transferability is ensured with mainly public data. The workshop discussion revealed three values: economic sustainability, environmental sustainability, and local energy autonomy. A total of eight alternatives for the 2030 energy system are identified to achieve these values. We find that an alternative that seeks only maximization of economic sustainability should be rejected based on elicited preferences. Instead, several alternatives seeking a maximization of environmental sustainability with constraints on economic sustainability (i.e. total cost) and local energy autonomy consistently achieve the highest overall performance scores. A maximization of economic sustainability or local energy autonomy alone results in the lowest overall performance scores and should therefore not be pursued by the community. The intermediate alternatives demonstrate that an equivalent performance gain with respect to autonomy comes at higher costs than the same gain with respect to environmental sustainability. Similarities between the best performing alternatives in terms of technologies that can be installed by 2030 show that our methodology can generate concrete and robust recommendations on building-level measures for energy system design.}, + keywords = {Community operational research,MCDA,MILP,Sustainable energy,Uncertainties}, + file = {/home/sam/snap/zotero-snap/common/Zotero/storage/N4X7U5L5/McKenna et al. - 2018 - Combining local preferences with multi-criteria de.pdf;/home/sam/snap/zotero-snap/common/Zotero/storage/DFTWLY3S/S0377221718300729.html} } -@article{pfenninger_energy_2014, - title = {Energy systems modeling for twenty-first century energy challenges}, - volume = {33}, - issn = {1364-0321}, - url = {https://www.sciencedirect.com/science/article/pii/S1364032114000872}, - doi = {10.1016/j.rser.2014.02.003}, - abstract = {Energy systems models are important methods used to generate a range of insight and analysis on the supply and demand of energy. Developed over the second half of the twentieth century, they are now seeing increased relevance in the face of stringent climate policy, energy security and economic development concerns, and increasing challenges due to the changing nature of the twenty-first century energy system. In this paper, we look particularly at models relevant to national and international energy policy, grouping them into four categories: energy systems optimization models, energy systems simulation models, power systems and electricity market models, and qualitative and mixed-methods scenarios. We examine four challenges they face and the efforts being taken to address them: (1) resolving time and space, (2) balancing uncertainty and transparency, (3) addressing the growing complexity of the energy system, and (4) integrating human behavior and social risks and opportunities. In discussing these challenges, we present possible avenues for future research and make recommendations to ensure the continued relevance for energy systems models as important sources of information for policy-making.}, - pages = {74--86}, - journaltitle = {Renewable and Sustainable Energy Reviews}, - shortjournal = {Renewable and Sustainable Energy Reviews}, - author = {Pfenninger, Stefan and Hawkes, Adam and Keirstead, James}, - urldate = {2023-01-16}, - date = {2014-05-01}, - langid = {english}, - keywords = {Complexity, Energy policy, Energy systems modeling, High-resolution modeling, Uncertainty}, - file = {ScienceDirect Full Text PDF:/Users/samdotson/Zotero/storage/ZB9UXEB5/Pfenninger et al. - 2014 - Energy systems modeling for twenty-first century e.pdf:application/pdf;ScienceDirect Snapshot:/Users/samdotson/Zotero/storage/WG6LL95H/S1364032114000872.html:text/html}, +@misc{nationalrenewableenergylaboratory2023AnnualTechnology2023, + title = {2023 {{Annual Technology Baseline}} ({{ATB}})}, + author = {{National Renewable Energy Laboratory}}, + year = {2023}, + urldate = {2024-02-26}, + file = {/home/sam/snap/zotero-snap/common/Zotero/storage/URRFWYVN/data.html} } -@article{vagero_can_2023, - title = {Can we optimise for justice? Reviewing the inclusion of energy justice in energy system optimisation models}, - volume = {95}, - issn = {2214-6296}, - url = {https://www.sciencedirect.com/science/article/pii/S2214629622004169}, - doi = {10.1016/j.erss.2022.102913}, - shorttitle = {Can we optimise for justice?}, - abstract = {Energy systems optimisation models are used for analysing energy systems and questions of e.g. greenhouse gas mitigation aligned with the Paris Agreement. However, the techno-economic nature of energy system models has opened for discussions about how well societal aspects are represented. Studying justice implications in energy systems modelling is an opportunity to inform policy-makers and the broader society how long-term changes to energy systems may affect different social groups and how to minimise injustices. In this paper, we review how, and to what extent, aspects of social justice have been included in energy systems optimisation modelling as well as areas for future research. In addition to reviewing published journal articles and reports, we organise a workshop with energy system modellers and social scientists, providing qualitative information on past approaches and potential future venues. We identify 3 key findings: (i) Exploring alternative system configurations to cost-optimality is receiving increasing attention and typically done through a ‘modelling to generate alternatives’ approach, (ii) among formalised definitions of distributional justice, equality (equal distribution) is the most common equity principle. There is at the same time little reflection on the choice and impact of equity principles, potentially contributing to an overly narrow understanding of justice. (iii) Among the workshop participants, participatory approaches which involves stakeholders and lay people are considered a potential future area of research, especially for making modelling results and processes more accessible and impactful to the wider public.}, - pages = {102913}, - journaltitle = {Energy Research \& Social Science}, - shortjournal = {Energy Research \& Social Science}, - author = {Vågerö, Oskar and Zeyringer, Marianne}, - urldate = {2024-02-19}, - date = {2023-01-01}, - keywords = {Energy justice, Optimisation, Energy systems modelling, Social justice}, - file = {Full Text:/Users/samdotson/Zotero/storage/EB5R8QJC/Vågerö and Zeyringer - 2023 - Can we optimise for justice Reviewing the inclusi.pdf:application/pdf}, +@article{pfenningerEnergySystemsModeling2014, + title = {Energy Systems Modeling for Twenty-First Century Energy Challenges}, + author = {Pfenninger, Stefan and Hawkes, Adam and Keirstead, James}, + year = {2014}, + month = may, + journal = {Renewable and Sustainable Energy Reviews}, + volume = {33}, + pages = {74--86}, + issn = {1364-0321}, + doi = {10.1016/j.rser.2014.02.003}, + urldate = {2023-01-16}, + abstract = {Energy systems models are important methods used to generate a range of insight and analysis on the supply and demand of energy. Developed over the second half of the twentieth century, they are now seeing increased relevance in the face of stringent climate policy, energy security and economic development concerns, and increasing challenges due to the changing nature of the twenty-first century energy system. In this paper, we look particularly at models relevant to national and international energy policy, grouping them into four categories: energy systems optimization models, energy systems simulation models, power systems and electricity market models, and qualitative and mixed-methods scenarios. We examine four challenges they face and the efforts being taken to address them: (1) resolving time and space, (2) balancing uncertainty and transparency, (3) addressing the growing complexity of the energy system, and (4) integrating human behavior and social risks and opportunities. In discussing these challenges, we present possible avenues for future research and make recommendations to ensure the continued relevance for energy systems models as important sources of information for policy-making.}, + langid = {english}, + keywords = {Complexity,Energy policy,Energy systems modeling,High-resolution modeling,Uncertainty}, + file = {/home/sam/snap/zotero-snap/common/Zotero/storage/ZB9UXEB5/Pfenninger et al. - 2014 - Energy systems modeling for twenty-first century e.pdf;/home/sam/snap/zotero-snap/common/Zotero/storage/WG6LL95H/S1364032114000872.html} } -@article{mckenna_combining_2018, - title = {Combining local preferences with multi-criteria decision analysis and linear optimization to develop feasible energy concepts in small communities}, - volume = {268}, - issn = {0377-2217}, - url = {https://www.sciencedirect.com/science/article/pii/S0377221718300729}, - doi = {10.1016/j.ejor.2018.01.036}, - series = {Community Operational Research: Innovations, internationalization and agenda-setting applications}, - abstract = {Decentralised community energy resources are often abundant in smaller, more rural communities. Such communities often lack the capacity to develop extensive energy concepts and thus to exploit these resources in a consistent way. This paper presents an integrated participatory approach to developing feasible energy concepts for small communities. The novelty lies in the combination of methods, the consideration of uncertainties, and the application to an exemplary municipality in Germany. Stakeholder workshops are combined with energy modelling and multi-criteria decision analysis ({MCDA}), and a high transferability is ensured with mainly public data. The workshop discussion revealed three values: economic sustainability, environmental sustainability, and local energy autonomy. A total of eight alternatives for the 2030 energy system are identified to achieve these values. We find that an alternative that seeks only maximization of economic sustainability should be rejected based on elicited preferences. Instead, several alternatives seeking a maximization of environmental sustainability with constraints on economic sustainability (i.e. total cost) and local energy autonomy consistently achieve the highest overall performance scores. A maximization of economic sustainability or local energy autonomy alone results in the lowest overall performance scores and should therefore not be pursued by the community. The intermediate alternatives demonstrate that an equivalent performance gain with respect to autonomy comes at higher costs than the same gain with respect to environmental sustainability. Similarities between the best performing alternatives in terms of technologies that can be installed by 2030 show that our methodology can generate concrete and robust recommendations on building-level measures for energy system design.}, - pages = {1092--1110}, - number = {3}, - journaltitle = {European Journal of Operational Research}, - shortjournal = {European Journal of Operational Research}, - author = {{McKenna}, R. and Bertsch, V. and Mainzer, K. and Fichtner, W.}, - urldate = {2024-02-25}, - date = {2018-08-01}, - keywords = {Community operational research, {MCDA}, {MILP}, Sustainable energy, Uncertainties}, - file = {McKenna et al. - 2018 - Combining local preferences with multi-criteria de.pdf:/Users/samdotson/Zotero/storage/N4X7U5L5/McKenna et al. - 2018 - Combining local preferences with multi-criteria de.pdf:application/pdf;ScienceDirect Snapshot:/Users/samdotson/Zotero/storage/DFTWLY3S/S0377221718300729.html:text/html}, +@misc{pfenningerOpenmodOpenEnergy, + title = {Openmod - {{Open Energy Modelling Initiative}}}, + author = {Pfenninger, Stefan and Schlect, Ingmar and Trondle, Tim and Brown, Tom}, + journal = {openmod-initiative}, + urldate = {2022-12-13}, + abstract = {The Open Energy Modelling (openmod) Initiative promotes open energy modelling across the world. Energy models are widely used for policy advice and research. They serve to help answer questions on energy policy, decarbonization, and transitions towards renewable energy sources. Currently, most energy models are black boxes -- even to fellow researchers. ``Open'' refers to model source code that can be studied, changed and improved as well as freely available energy system data. We believe that more openness in energy modelling increases transparency and credibility, reduces wasteful double-work and improves overall quality. This allows the community to advance the research frontier and gain the highest benefit from energy modelling for society. We, energy modelers from various institutions, want to promote the idea and practice of open energy modelling among fellow modelers, research institutions, funding bodies, and recipients of our work.}, + howpublished = {https://www.openmod-initiative.org/}, + file = {/home/sam/snap/zotero-snap/common/Zotero/storage/8FWR7W4F/www.openmod-initiative.org.html} } -@misc{national_renewable_energy_laboratory_2023_2023, - title = {2023 Annual Technology Baseline ({ATB})}, - url = {https://atb.nrel.gov/electricity/2023/data}, - author = {{National Renewable Energy Laboratory}}, - urldate = {2024-02-26}, - date = {2023}, - file = {Data | Electricity | 2023 | ATB | NREL:/Users/samdotson/Zotero/storage/URRFWYVN/data.html:text/html}, +@article{vageroCanWeOptimise2023, + title = {Can We Optimise for Justice? {{Reviewing}} the Inclusion of Energy Justice in Energy System Optimisation Models}, + shorttitle = {Can We Optimise for Justice?}, + author = {V{\aa}ger{\"o}, Oskar and Zeyringer, Marianne}, + year = {2023}, + month = jan, + journal = {Energy Research \& Social Science}, + volume = {95}, + pages = {102913}, + issn = {2214-6296}, + doi = {10.1016/j.erss.2022.102913}, + urldate = {2024-02-19}, + abstract = {Energy systems optimisation models are used for analysing energy systems and questions of e.g. greenhouse gas mitigation aligned with the Paris Agreement. However, the techno-economic nature of energy system models has opened for discussions about how well societal aspects are represented. Studying justice implications in energy systems modelling is an opportunity to inform policy-makers and the broader society how long-term changes to energy systems may affect different social groups and how to minimise injustices. In this paper, we review how, and to what extent, aspects of social justice have been included in energy systems optimisation modelling as well as areas for future research. In addition to reviewing published journal articles and reports, we organise a workshop with energy system modellers and social scientists, providing qualitative information on past approaches and potential future venues. We identify 3 key findings: (i) Exploring alternative system configurations to cost-optimality is receiving increasing attention and typically done through a `modelling to generate alternatives' approach, (ii) among formalised definitions of distributional justice, equality (equal distribution) is the most common equity principle. There is at the same time little reflection on the choice and impact of equity principles, potentially contributing to an overly narrow understanding of justice. (iii) Among the workshop participants, participatory approaches which involves stakeholders and lay people are considered a potential future area of research, especially for making modelling results and processes more accessible and impactful to the wider public.}, + keywords = {Energy justice,Energy systems modelling,Optimisation,Social justice}, + file = {/home/sam/snap/zotero-snap/common/Zotero/storage/EB5R8QJC/Vågerö and Zeyringer - 2023 - Can we optimise for justice Reviewing the inclusi.pdf} } diff --git a/paper.md b/paper.md index 7b45512..6a5e2df 100644 --- a/paper.md +++ b/paper.md @@ -1,5 +1,5 @@ --- -title: 'Osier: A Python package for multi-objective energy system optimization' +title: '`Osier`: A Python package for multi-objective energy system optimization' tags: - Python - energy systems @@ -8,7 +8,7 @@ tags: authors: - name: Samuel G. Dotson - orcid: 0000-0002-8662-0336 - - affiliation: 1 + - affiliation: 1,2 - corresponding: true affiliations: - name: Felix T. Adler Fellow, Nuclear, Plasma, and Radiological Engineering, University of Illinois Urbana-Champaign, USA @@ -24,13 +24,13 @@ Transitioning to a clean energy economy will require expanded energy infrastruct `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 with procedural and recognition justice. # Statement of Need There are myriad open- and closed-source energy system optimization models -(ESOMs) available `@pfenninger_openmod_nodate`. ESOMs can be used for a variety +(ESOMs) available [`@pfenningerOpenmodOpenEnergy`]. 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 two important characteristics: Single objective optimization and an economic objective (either cost minimization or social welfare maximization). Simultaneously, there is growing awareness of energy justice and calls for its inclusion in energy models `@pfenninger_energy_2014`. +meant to guide decision-makers in planning processes. However, despite the many available models, all of these tools share two important characteristics: Single objective optimization and an economic objective (either cost minimization or social welfare maximization). Simultaneously, there is growing awareness of energy justice and calls for its inclusion in energy models [`@pfenninger_energy_2014`]. # 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 `@national_renewable_energy_laboratory_2023_2023`. +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 [`@national_renewable_energy_laboratory_2023_2023`]. 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. 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`.