further_reading.bib

@techreport{EISecLegCOMTNM2019,
  author      = {Mike O'{B}oyle and Ron Lehr},
  abstract    = {This issue brief compares new securitization legislation in Colorado, Montana, and
                 New Mexico to refinance utility investments in early-retired electric generation
                 plants. As similar legislation is considered in other states, these comparisons can
                 support legislators deciding how much protection to offer consumers and how to
                 evaluate public interest values and utility interests.},
  title       = {Comparing 2019 Securitization Legislation In Colorado, Montana, and New Mexico},
  institution = {Energy Innovation},
  year        = {2020},
  url         = {https://energyinnovation.org/publication/comparing-2019-securitization-legislation-in-colorado-montana-and-new-mexico/},
  urldate     = {2021-10-12},
  type        = {Report}
}

@article{AbernathayARCO,
  author   = {Abernathey, Ryan P. and
              Augspurger, Tom and
              Banihirwe, Anderson and
              Blackmon-Luca, Charles C. and
              Crone, Timothy J. and
              Gentemann, Chelle L. and
              Hamman, Joseph J. and
              Henderson, Naomi and
              Lepore, Chiara and
              McCaie, Theo A. and
              Robinson, Niall H. and
              Signell, Richard P.},
  journal  = {Computing in Science & Engineering},
  title    = {Cloud-Native Repositories for Big Scientific Data},
  year     = {2021},
  volume   = {23},
  number   = {2},
  pages    = {26-35},
  abstract = {Scientific data have traditionally been distributed via downloads from data server
              to local computer. This way of working suffers from limitations as scientific
              datasets grow toward the petabyte scale. A “cloud-native data repository,” as
              defined in this article, offers several advantages over traditional data
              repositories-performance, reliability, cost-effectiveness, collaboration,
              reproducibility, creativity, downstream impacts, and access and inclusion. These
              objectives motivate a set of best practices for cloud-native data repositories:
              analysis-ready data, cloud-optimized (ARCO) formats, and loose coupling with
              data-proximate computing. The Pangeo Project has developed a prototype
              implementation of these principles by using open-source scientific Python tools. By
              providing an ARCO data catalog together with on-demand, scalable distributed
              computing, Pangeo enables users to process big data at rates exceeding 10 GB/s.
              Several challenges must be resolved in order to realize cloud computing's full
              potential for scientific research, such as organizing funding, training users, and
              enforcing data privacy requirements.},
  doi      = {10.1109/MCSE.2021.3059437},
  url      = {https://doi.org/10.1109/MCSE.2021.3059437},
  issn     = {1558-366X},
  month    = {March}
}

@inproceedings{ModelNotDataWork,
  doi       = {10.1145/3411764.3445518},
  author    = {Sambasivan, Nithya and
               Kapania, Shivani and
               Highfill, Hannah and
               Akrong, Diana and
               Paritosh, Praveen and
               Aroyo, Lora M},
  title     = {“{E}veryone wants to do the model work, not the data work”: Data Cascades in High-Stakes {A}{I}},
  year      = {2021},
  isbn      = {9781450380966},
  publisher = {Association for Computing Machinery},
  address   = {New York, NY, USA},
  url       = {https://doi.org/10.1145/3411764.3445518},
  abstract  = {AI models are increasingly applied in high-stakes domains like health and
               conservation.  Data quality carries an elevated significance in high-stakes AI due
               to its heightened downstream impact, impacting predictions like cancer detection,
               wildlife poaching, and loan allocations. Paradoxically, data is the most
               under-valued and de-glamorised aspect of AI. In this paper, we report on data
               practices in high-stakes AI, from interviews with 53 AI practitioners in India,
               East and West African countries, and USA. We define, identify, and present
               empirical evidence on Data Cascades—compounding events causing negative,
               downstream effects from data issues—triggered by conventional AI/ML practices that
               undervalue data quality. Data cascades are pervasive (92% prevalence), invisible,
               delayed, but often avoidable. We discuss HCI opportunities in designing and
               incentivizing data excellence as a first-class citizen of AI, resulting in safer
               and more robust systems for all.},
  booktitle = {Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems},
  articleno = {39},
  numpages  = {15}
}

@book{stokes2020short,
  title     = {Short Circuiting Policy: Interest Groups and the Battle Over Clean Energy and Climate Policy in the American States},
  author    = {Stokes, Leah Cardmore},
  isbn      = {9780190074258},
  lccn      = {2019047241},
  series    = {Studies in Postwar American Political Development Series},
  url       = {https://books.google.com/books?id=uLTRDwAAQBAJ},
  year      = {2020},
  publisher = {Oxford University Press}
}

@book{CullenwardVictor2020,
  title     = {Making Climate Policy Work},
  author    = {Cullenward, Danny and Victor, David G.},
  isbn      = {9781509541805},
  url       = {https://www.wiley.com/en-us/Making+Climate+Policy+Work-p-9781509541805},
  year      = {2020},
  publisher = {John Wiley & Sons, Ltd},
  abstract = {For decades, the world's governments have struggled to move from talk to
    action on climate. Many now hope that growing public concern will lead to greater
    policy ambition, but the most widely promoted strategy to address the climate crisis
    -- the use of market-based programs -- hasn't been working and isn't ready to scale.
    Danny Cullenward and David Victor show how the politics of creating and maintaining
    market-based policies render them ineffective nearly everywhere they have been
    applied. Reforms can help around the margins, but markets' problems are structural
    and won't disappear with increasing demand for climate solutions. Facing that
    reality requires relying more heavily on smart regulation and industrial policy --
    government-led strategies -- to catalyze the transformation that markets promise,
    but rarely deliver.
  }
}

@techreport{IERLCOE2016,
  author      = {Thomas F. Stacey and George S. Taylor},
  title       = {The Levelized Cost of Electricity from Existing Generation Resources},
  institution = {Institute for Energy Research},
  year        = {2016},
  month       = {July},
  url         = {https://www.instituteforenergyresearch.org/wp-content/uploads/2016/07/IER_LCOE_2016-2.pdf},
  urldate     = {2021-10-15}
}

@techreport{IERLCOE2019,
  author      = {Thomas F. Stacey and George S. Taylor},
  title       = {The Levelized Cost of Electricity from Existing Generation Resources},
  institution = {Institute for Energy Research},
  year        = {2019},
  month       = {June},
  url         = {https://www.instituteforenergyresearch.org/wp-content/uploads/2019/06/IER_LCOE2019Final-.pdf},
  urldate     = {2021-10-15}
}

@misc{PSCo2021ERP,
  author       = {Public Service Company of Colorado},
  title        = {2021 Clean Energy Plan},
  institution  = {State of Colorado Department of Regulatory Affairs},
  howpublished = {Electronic filing 21A-0141E},
  year         = {2021},
  url          = {https://www.xcelenergy.com/company/rates_and_regulations/resource_plans/clean_energy_plan},
  urldate      = {2021-10-15}
}

@techreport{DataCiteV4.4,
  author      = {DataCite Metadata Working Group},
  title       = {DataCite Metadata Schema Documentation for the Publication and Citation of Research Data and Other Research Outputs},
  institution = {DataCite e.V.},
  year        = {2021},
  doi         = {10.14454/3w3z-sa82},
  version     = {4.4},
  url         = {https://doi.org/10.14454/3w3z-sa82},
  urldate     = {2021-10-15}
}

@techreport{FossilFuelRacism,
  author      = {Tim Donaghy and Charlie Jiang and Colette Pichon Battle and Emma Collin and Ryan Schleeter and Janet Redman},
  title       = {Fossil Fuel Racism: How Phasing out Oil, Gas, and Coal Can Protect Communities},
  institution = {Greenpeace USA},
  year        = {2021},
  url         = {https://www.greenpeace.org/usa/reports/fossil-fuel-racism/},
  urldate     = {2021-10-15}
}

@techreport{EnergyBurdensACEEE2020,
  author      = {Ariel Drehobl and Lauren Ross and Roxanna Ayala},
  title       = {How High are Household Energy Burdens?},
  institution = {American Council for an Energy-Efficient Economy},
  year        = {2020},
  url         = {https://www.aceee.org/research-report/u2006},
  urldate     = {2021-10-15}
}

@article{HighEnergyBurden2020,
  doi       = {10.1088/2516-1083/abb954},
  url       = {https://doi.org/10.1088/2516-1083/abb954},
  year      = 2020,
  month     = {oct},
  journal   = {Progress in Energy},
  publisher = {{IOP} Publishing},
  volume    = {2},
  number    = {4},
  pages     = {042003},
  author    = {Marilyn A Brown and Anmol Soni and Melissa V Lapsa and Katie Southworth and Matt Cox},
  title     = {High energy burden and low-income energy affordability: conclusions from a literature review},
  abstract  = {In an era of U.S. energy abundance, the persistently high energy bills
               paid by low-income households is troubling. After decades of weatherization and
               bill-payment programs, low-income households still spend a higher percent of their
               income on electricity and gas bills than any other income group. Their energy burden
               is not declining, and it remains persistently high in particular geographies such as
               the South, rural America, and minority communities. As public agencies and utilities
               attempt to transition to a sustainable energy future, many of the programs that
               promote energy efficiency, rooftop solar, electric vehicles, and home batteries are
               largely inaccessible to low-income households due to affordability barriers. This
               review describes the ecosystem of stakeholders and programs, and identifies promising
               opportunities to address low-income energy affordability, such as behavioral
               economics, data analytics, and leveraging health care benefits. Scalable approaches
               require linking programs and policies to tackle the complex web of causes and impacts
               faced by financially constrained households.}
}

@techreport{SierraClubFinancialTools,
  author      = {Uday Varadarajan and David Posner and Jeremy Fisher},
  title       = {Harnessing Financial Tools to Transform the Electric Sector},
  institution = {Sierra Club},
  year        = {2018},
  url         = {https://www.sierraclub.org/sites/www.sierraclub.org/files/sierra-club-harnessing-financial-tools-electric-sector.pdf},
  urldate     = {2021-10-15}
}

@techreport{EIDebtForEquity,
  author      = {Ron Lehr and Mike O'{B}oyle},
  title       = {Debt for Equity Utility Refinance},
  institution = {Energy Innovation},
  year        = {2018},
  url         = {https://energyinnovation.org/wp-content/uploads/2018/11/Debt-for-Equity-Issue-Brief_12.3.18.pdf},
  urldate     = {2021-10-15}
}

@techreport{EISteelForFuel,
  author      = {Ron Lehr and Mike O'{B}oyle},
  title       = {Steel for Fuel: Opportunities for Investors and Customers},
  institution = {Energy Innovation},
  year        = {2018},
  url         = {https://energyinnovation.org/wp-content/uploads/2018/11/Steel-for-Fuel-Brief_12.3.18.pdf},
  urldate     = {2021-10-15}
}

@techreport{EIDepreciationRetirements,
  author      = {Ron Lehr and Mike O'{B}oyle},
  title       = {Depreciation and Early Plant Retirements},
  institution = {Energy Innovation},
  year        = {2018},
  url         = {https://energyinnovation.org/wp-content/uploads/2018/12/Depreciation-and-Early-Plant-Retirements-Brief_12.3.2018.pdf},
  urldate     = {2021-10-15}
}

@article{doi:10.1021/acs.est.9b04522,
author = {Rossol, Michael and Brinkman, Gregory and Buster, Grant and Denholm, Paul and Novacheck, Joshua and Stephen, Gord},
title = {An Analysis of Thermal Plant Flexibility Using a National Generator Performance Database},
journal = {Environmental Science \& Technology},
volume = {53},
number = {22},
pages = {13486-13494},
year = {2019},
doi = {10.1021/acs.est.9b04522},
note ={PMID: 31644271},
URL = {https://doi.org/10.1021/acs.est.9b04522},
eprint = {https://doi.org/10.1021/acs.est.9b04522},
abstract = {Grid integration studies are key to understanding our ability to integrate
            variable generation resources into the power system and evaluating the
            associated costs and benefits. In these studies, it is important to
            understand the flexibility of the thermal power fleet, including how thermal
            plants operate at part load. Without a comprehensive understanding of
            thermal plant operation, we may over- or underestimate our ability to
            integrate variable generation resources and thus draw incomplete or
            inaccurate conclusions regarding their potential economic and environmental
            effects. The only public data source for understanding many elements of the
            operational characteristics of the thermal fleet is the U.S. Environmental
            Protection Agency Clean Air Markets database of historical power plant
            operation. However, though these data sets have been widely utilized, their
            use has proven to be difficult, and methods to clean and filter the data are
            not transparent. Here, we describe the database and a method to clean and
            filter it. We then use the cleaned database to demonstrate several
            characteristics of historical plant operation, including frequent part load
            operation.  Finally, we provide a cleaned data set with heat rate curves and
            describe how to use it in general modeling activities and analysis.}
}

@article{HIRTH2020100433,
title = {Open data for electricity modeling: Legal aspects},
journal = {Energy Strategy Reviews},
volume = {27},
pages = {100433},
year = {2020},
issn = {2211-467X},
doi = {10.1016/j.esr.2019.100433},
url = {https://doi.org/10.1016/j.esr.2019.100433},
author = {Lion Hirth},
keywords = {Open data, Electricity system analysis,  database right, Copyright},
abstract = {Power system modeling is data intensive. In Europe, electricity system data
            is often available from sources such as statistical offices or system
            operators.  However, it is often unclear if these data can be legally used
            for modeling, and in particular if such use infringes intellectual property
            rights. This article reviews the legal status of power system data, both as
            a guide for data users and for data publishers. It is based on
            interpretation of the law, a review of the secondary literature, an analysis
            of the licenses used by major data distributors, expert interviews, and a
            series of workshops. A core finding is that in many cases the legality of
            current practices is doubtful: in fact, it seems likely that modelers
            infringe intellectual property rights quite regularly. This is true for
            industry analysis but also academic researchers. A straightforward solution
            is open data – the idea that data can be freely used, modified, and shared
            by anyone for any purpose. To be open, it is not sufficient for data to be
            accessible free of cost, it must also come with an open data license, the
            most common types of which are also reviewed in this paper.}
}

@book{10.1145/3310205,
author = {Ilyas, Ihab F. and Chu, Xu},
title = {Data Cleaning},
year = {2019},
isbn = {9781450371520},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
doi = {10.1145/3310205},
url = {https://dl.acm.org/doi/book/10.1145/3310205},
abstract = {Data quality is one of the most important problems in data management, since
    dirty data often leads to inaccurate data analytics results and incorrect business
    decisions. Poor data across businesses and the U.S. government are reported to cost
    trillions of dollars a year. Multiple surveys show that dirty data is the most
    common barrier faced by data scientists. Not surprisingly, developing effective and
    efficient data cleaning solutions is challenging and is rife with deep theoretical
    and engineering problems.This book is about data cleaning, which is used to refer to
    all kinds of tasks and activities to detect and repair errors in the data. Rather
    than focus on a particular data cleaning task, we give an overview of the endto- end
    data cleaning process, describing various error detection and repair methods, and
    attempt to anchor these proposals with multiple taxonomies and views. Specifically,
    we cover four of the most common and important data cleaning tasks, namely, outlier
    detection, data transformation, error repair (including imputing missing values),
    and data deduplication. Furthermore, due to the increasing popularity and
    applicability of machine learning techniques, we include a chapter that specifically
    explores how machine learning techniques are used for data cleaning, and how data
    cleaning is used to improve machine learning models.This book is intended to serve
    as a useful reference for researchers and practitioners who are interested in the
    area of data quality and data cleaning. It can also be used as a textbook for a
    graduate course. Although we aim at covering state-of-the-art algorithms and
    techniques, we recognize that data cleaning is still an active field of research and
    therefore provide future directions of research whenever appropriate.}
}

@book{doi:https://doi.org/10.1002/0470036427,
publisher = {John Wiley & Sons, Ltd},
isbn = {9780470036426},
author = {von Meier, Alexandra},
title = {Electric Power Systems: A Conceptual Introduction},
doi = {10.1002/0470036427},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/0470036427},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/0470036427},
year = {2006},
keywords = {physics of electricity, circuit fundamentals, electric and magnetic fields},
abstract = {Electric Power Systems explains and illustrates how the electric grid works
    in a clear, straightforward style that makes highly technical material accessible.
    It begins with a thorough discussion of the underlying physical concepts of
    electricity, circuits, and complex power that serves as a foundation for more
    advanced material.  Readers are then introduced to the main components of electric
    power systems, including generators, motors and other appliances, and transmission
    and distribution equipment such as power lines, transformers, and circuit breakers.
    The author explains how a whole power system is managed and coordinated, analyzed
    mathematically, and kept stable and reliable.}
}

@misc{brown_2019_3368397,
  author       = {Brown, Patrick R. and O'Sullivan, Francis M.},
  title        = {{Shaping photovoltaic array output to align with changing wholesale electricity price profiles}},
  month        = sep,
  year         = 2019,
  publisher    = {Zenodo},
  version      = {1.0.0},
  doi          = {10.5281/zenodo.3368397},
  url          = {https://doi.org/10.5281/zenodo.3368397}
}

@misc{brown_2019_3562896,
  author       = {Brown, Patrick R.},
  title        = {{Spatial and temporal variation in the value of solar power across United States electricity markets}},
  month        = dec,
  year         = 2019,
  publisher    = {Zenodo},
  version      = {1.0.0},
  doi          = {10.5281/zenodo.3562896},
  url          = {https://doi.org/10.5281/zenodo.3562896}
}

@article{BROWN2019113734,
title = {Shaping photovoltaic array output to align with changing wholesale electricity price profiles},
journal = {Applied Energy},
volume = {256},
pages = {113734},
year = {2019},
issn = {0306-2619},
doi = {https://doi.org/10.1016/j.apenergy.2019.113734},
url = {https://www.sciencedirect.com/science/article/pii/S0306261919314217},
author = {Patrick R. Brown and Francis M. O’Sullivan},
keywords = {Solar energy, Photovoltaics, Locational marginal pricing, Electricity markets, Curtailment},
abstract = {Large-scale deployment of solar photovoltaics (PV) contributes to the
    occurrence of depressed—and sometimes negative—electricity prices during daylight
    hours as PV displaces higher-cost generators in the merit-order dispatch stack.
    These changes in electricity price provide an opportunity to increase the wholesale
    energy revenue of PV generators through temporal shaping of PV output. Here, we
    explore the impact of three output-shaping strategies on PV wholesale energy revenue
    and capacity factor: utilization of 1-axis tracking, curtailment during
    negative-price hours, and modification of fixed-tilt array orientation.
    Utility-scale PV arrays are modeled at more than 10,000 pricing nodes across six
    United States electricity markets over the 2010–2017 time period. Large changes in
    revenue-optimized output profiles are observed for the California system, where
    solar capacity penetration has increased from ∼2% of peak load in 2010 to ∼28% of
    peak load in 2017, and the wholesale revenue benefits of temporal output shaping are
    increasing with time. On the California real-time market in 2017, compared to
    capacity-factor-optimized fixed-tilt arrays with must-run operation, curtailment
    increases revenues by 9%, curtailment in conjunction with fixed-tilt orientation
    optimization increases revenues by 20%, 1-axis tracking without curtailment
    increases revenues by 32%, and 1-axis tracking with curtailment increases revenues
    by 42% for the median node. Median optimal fixed-tilt azimuths for PV on the
    real-time market in California have increased from 192° in 2010 to 235° (i.e. 55°
    west of south) in 2017. Among the markets and years studied, the California market
    in 2017 demonstrates the largest potential benefit from temporal output shaping.
    These results highlight mechanisms for mitigating some of the decline in PV
    wholesale value at high solar penetrations, and illustrate the importance of
    adapting PV installation and dispatch strategies to changing power system
    conditions.}
}

@article{BROWN2020109594,
title = {Spatial and temporal variation in the value of solar power across United States electricity markets},
journal = {Renewable and Sustainable Energy Reviews},
volume = {121},
pages = {109594},
year = {2020},
issn = {1364-0321},
doi = {https://doi.org/10.1016/j.rser.2019.109594},
url = {https://www.sciencedirect.com/science/article/pii/S1364032119308020},
author = {Patrick R. Brown and Francis M. O'Sullivan},
keywords = {Solar energy, Photovoltaics, Value of solar, Locational marginal price, Distributed energy resources, Merit order effect, Air pollution, Capacity value, Resource adequacy},
abstract = {The cost of utility-scale photovoltaics (PV) has declined rapidly over the
    past decade. Yet increased renewable electricity generation, decreased natural gas
    prices, and deployment of emissions-control technology across the United States have
    led to concurrent changes in electricity prices and power system emissions rates,
    each of which influence the value of PV electricity. An ongoing assessment of the
    economic competitiveness of PV is therefore necessary as PV cost and value continue
    to evolve. Here, we use historical nodal electricity prices, capacity market prices,
    marginal power system emissions rates of CO2 and air pollutants, and weather data to
    model the energy, capacity, health, and climate value of PV electricity at over
    10 000 locations across six U.S. Independent System Operators (ISOs) from 2010 to
    2017. On the energy and capacity markets, transmission congestion in some locations
    and years results in PV revenues that are more than double the median across the
    relevant ISO. While the marginal public health benefits from avoided SO2, NOx, and
    PM2.5 emissions have declined over time in most ISOs, monetizing the health benefits
    of PV generation in 2017 would increase median PV energy revenues by 70% in MISO and
    NYISO and 100% in PJM. Given 2017 PV costs, electricity prices, and grid conditions,
    PV breaks even at 30% of modeled locations on the basis of energy, capacity, and
    health benefits, at 75% of modeled locations with the addition of a 50 $/ton CO2
    price, and at 100% of modeled locations with a 100 $/ton CO2 price. These results
    suggest that PV cost decline has outpaced value decline over the past decade, such
    that in 2017 the net benefits of utility-scale PV outweigh the cost at the majority
    of modeled locations.}
}